%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from mpl_toolkits.basemap import Basemap
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from scipy.stats import ttest_ind

pd.set_option('display.max_rows', 200)
pd.set_option('display.max_columns', 200)

Football Players' Price Prediction. Analysing Some of Their Features.¶

We will analyse Fifa dataset for two years 2016 and 2020. It is obtained from Kaggle. There are several notebooks using this dataset in Kaggle. Good examples are: Notebook 1, Notebook 2 and Notebook 3. The 2016 data will be used for preliminary analysis and training a linear regression model. Then, we will try to predict players' value in 2020. This will not be a time series analysis, but rather an attempt to tell price based on changed characteristics of the players like age, club, abilities which can improve or deteriorate (in case of injury for example). The structure of the work is as following:

Description of the dataset
Importing and cleaning the data
Perform EDA
Create hypothesis and linear regression model and
Check hypothesis and test model with new data

Description of the dataset¶

Most of the column names are self explanatory. I will mention only those which are not understandable for the general public. To start with - the group of: 'team_positions','ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'.

These are abbreviations of players' positions on the field. Best they can be understood is by looking at the following picture.

playground

Import and Clean the data¶

players_16=pd.read_csv("data/players_16.csv",index_col="sofifa_id")

players_16.head()

players_16.shape

(14881, 103)

From the database we can see that some of the columns will not be necessary for the analysis. They are: "player_url","long_name" (we have "short_name"), "wage_eur". Usually, wage is directly related to value, so either we know both or try to guess them. Makes no sense to make prediction for one based on the knowledge of the other. For the same reason, "Release clause" should also be removed. It is determined together with the value. Anyway, in this dataset there is no info about it.

Let's now see some general information about the dataset.

players_16.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 14881 entries, 158023 to 11728
Columns: 103 entries, player_url to rb
dtypes: float64(17), int64(10), object(76)
memory usage: 11.8+ MB

players_16.isnull().sum().sort_values(ascending=False)

mentality_composure           14881
release_clause_eur            14881
nation_jersey_number          13871
nation_position               13871
loaned_from                   13827
player_tags                   13625
gk_diving                     13263
gk_handling                   13263
gk_kicking                    13263
gk_reflexes                   13263
gk_speed                      13263
gk_positioning                13263
player_traits                  8119
rcb                            1618
ls                             1618
dribbling                      1618
shooting                       1618
pace                           1618
rs                             1618
passing                        1618
physic                         1618
st                             1618
rb                             1618
lw                             1618
rm                             1618
lam                            1618
cam                            1618
ram                            1618
lm                             1618
lcm                            1618
cm                             1618
rcm                            1618
lwb                            1618
lf                             1618
ldm                            1618
cdm                            1618
rdm                            1618
rwb                            1618
lb                             1618
lcb                            1618
cb                             1618
rw                             1618
defending                      1618
rf                             1618
cf                             1618
joined                         1225
team_position                   171
team_jersey_number              171
contract_valid_until            171
skill_moves                       0
short_name                        0
long_name                         0
age                               0
dob                               0
height_cm                         0
weight_kg                         0
nationality                       0
club                              0
potential                         0
overall                           0
work_rate                         0
value_eur                         0
wage_eur                          0
player_positions                  0
preferred_foot                    0
international_reputation          0
weak_foot                         0
body_type                         0
real_face                         0
skill_long_passing                0
attacking_crossing                0
power_strength                    0
goalkeeping_reflexes              0
goalkeeping_positioning           0
goalkeeping_kicking               0
goalkeeping_handling              0
goalkeeping_diving                0
defending_sliding_tackle          0
defending_standing_tackle         0
defending_marking                 0
mentality_penalties               0
mentality_vision                  0
mentality_positioning             0
mentality_interceptions           0
mentality_aggression              0
power_long_shots                  0
power_stamina                     0
attacking_finishing               0
power_jumping                     0
power_shot_power                  0
movement_balance                  0
movement_reactions                0
movement_agility                  0
movement_sprint_speed             0
movement_acceleration             0
skill_ball_control                0
skill_fk_accuracy                 0
skill_curve                       0
skill_dribbling                   0
attacking_volleys                 0
attacking_short_passing           0
attacking_heading_accuracy        0
player_url                        0
dtype: int64

There are some features which have Null values for quite a large amount of records. "mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags" have 91.6-100% missing data, so they can be removed. "gk_kicking", "gk_reflexes", "gk_speed", "gk_positioning", "gk_diving", "gk_handling" also have many null values but that is because the features are related to goal-keepers which are naturally much less than the field players. So we will keep them for now.

#Remove unnecessary columns

players_16=players_16.drop(["player_url","long_name","wage_eur","release_clause_eur","mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags"],axis=1)

players_16.head()

players_16.shape

(14881, 94)

players_16.describe().transpose()

These are the statistics for the numerical features. One of them is considered a numeral value but in fact its meaning is categorical - team_jersey_number. This should be taken into account later, in the preparation for submitting the dataset to the ML algorithm. Besides, there are several attributes which are in fact numerical values but due to the way they are input in the table, they are considered non numerical. These are the following columns: 'attacking_crossing', 'attacking_finishing', 'attacking_heading_accuracy', 'attacking_short_passing', 'attacking_volleys', 'skill_dribbling', 'skill_curve', 'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed', 'movement_agility', 'movement_reactions', 'movement_balance', 'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots', 'mentality_aggression', 'mentality_interceptions', 'mentality_positioning', 'mentality_vision', 'mentality_penalties', 'defending_marking', 'defending_standing_tackle', 'defending_sliding_tackle', 'goalkeeping_diving', 'goalkeeping_handling', 'goalkeeping_kicking', 'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs', 'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm', 'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb', 'rcb', 'rb'.

The values are written in the format "Number1+number2" (i.e. "78+1", "84+3"). Sample is shown below.

players_16[['attacking_crossing','attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']].sample(7)

The numbers added after the "+/-" sign come from international reputation of player. So, for analysis purposes they can be added/subtracted.

Before that, we have to handle the NaN values in several features. The ones that depict some abilitites of the players. Main reason for not having value is that either they are qualities of field players in the records for a goal keeper or vice versa. So, it is normal to replace them by zeroes.

One more thing is noticed while checking the data. There is a kind of duplication of data. For example, columns "gk_diving","gk_handling", "gk_kicking","gk_reflexes" and "gk_positioning" look quite similar to 'goalkeeping_diving','goalkeeping_handling', 'goalkeeping_kicking','goalkeeping_reflexes','goalkeeping_positioning'. The difference is that in the first group for the field players any rating about goalkeeping capabilities is missing, while in the second group there are some. We would better keep the second group because sometimes GK qualities are useful for field players.

players_16=players_16.drop(["gk_diving","gk_handling", "gk_kicking","gk_reflexes","gk_positioning"],axis=1)

Thus, the columns which need filling with zeroes for NaN values and then transforming this type of string "Number1+number2" into a mathematical calculation are the following ones, put in the list "cols_for_fill".

cols_for_fill=['pace',
       'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing',
       'attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']

The columns that need transformation are too many and it will be time consuming to do that manually. That's why we will create a function. It converts the values to string, substitutes "NaN" with "0" and then makes the string a mathematical expression (by using eval()).

def transform_columns_to_number(data_frame,columns_to_be_transformed):
    for col in columns_to_be_transformed:
        data_frame[col]=data_frame[col].astype(str)
        data_frame[col]=data_frame[col].replace("nan","0")
        data_frame[col]=pd.Series([eval(item) for item in data_frame[col].values]).values
    #return data_frame[col]

transform_columns_to_number(players_16,cols_for_fill)

players_16.head()

Need to check for missing data.

players_16.isnull().sum().sort_values(ascending=False)

player_traits                 8119
joined                        1225
contract_valid_until           171
team_jersey_number             171
team_position                  171
attacking_finishing              0
dribbling                        0
defending                        0
physic                           0
gk_speed                         0
attacking_crossing               0
attacking_heading_accuracy       0
shooting                         0
attacking_short_passing          0
attacking_volleys                0
skill_dribbling                  0
skill_curve                      0
skill_fk_accuracy                0
skill_long_passing               0
skill_ball_control               0
movement_acceleration            0
movement_sprint_speed            0
passing                          0
rb                               0
pace                             0
movement_reactions               0
age                              0
dob                              0
height_cm                        0
weight_kg                        0
nationality                      0
club                             0
overall                          0
potential                        0
value_eur                        0
player_positions                 0
preferred_foot                   0
international_reputation         0
weak_foot                        0
skill_moves                      0
work_rate                        0
body_type                        0
real_face                        0
movement_agility                 0
movement_balance                 0
rcb                              0
power_shot_power                 0
cf                               0
rf                               0
rw                               0
lam                              0
cam                              0
ram                              0
lm                               0
lcm                              0
cm                               0
rcm                              0
rm                               0
lwb                              0
ldm                              0
cdm                              0
rdm                              0
rwb                              0
lb                               0
lcb                              0
cb                               0
lf                               0
lw                               0
rs                               0
mentality_penalties              0
power_jumping                    0
power_stamina                    0
power_strength                   0
power_long_shots                 0
mentality_aggression             0
mentality_interceptions          0
mentality_positioning            0
mentality_vision                 0
defending_marking                0
st                               0
defending_standing_tackle        0
defending_sliding_tackle         0
goalkeeping_diving               0
goalkeeping_handling             0
goalkeeping_kicking              0
goalkeeping_positioning          0
goalkeeping_reflexes             0
ls                               0
short_name                       0
dtype: int64

"player_traits" for now we will leave as it is. Anyway, it will not be submitted to the ML algorithm. "joined","team_position" and "contract_valid_until" seems logical to substitute with the most common value. While "team_jersey_number" we would replace with "0". We again need a function which will do the work for us.

def fill_missing_data(dataframe,columns):
    for col in columns:
        dataframe[col]=dataframe[col].fillna(dataframe[col].mode()[0])

columns=["joined","team_position","contract_valid_until"]
fill_missing_data(players_16,columns)

players_16["team_jersey_number"]=players_16["team_jersey_number"].fillna(0)

players_16.isnull().sum().sort_values(ascending=False)

player_traits                 8119
rb                               0
movement_reactions               0
shooting                         0
passing                          0
dribbling                        0
defending                        0
physic                           0
gk_speed                         0
attacking_crossing               0
attacking_finishing              0
attacking_heading_accuracy       0
attacking_short_passing          0
attacking_volleys                0
skill_dribbling                  0
skill_curve                      0
skill_fk_accuracy                0
skill_long_passing               0
skill_ball_control               0
movement_acceleration            0
movement_sprint_speed            0
pace                             0
contract_valid_until             0
joined                           0
value_eur                        0
age                              0
dob                              0
height_cm                        0
weight_kg                        0
nationality                      0
club                             0
overall                          0
potential                        0
player_positions                 0
team_jersey_number               0
preferred_foot                   0
international_reputation         0
weak_foot                        0
skill_moves                      0
work_rate                        0
body_type                        0
real_face                        0
team_position                    0
movement_agility                 0
movement_balance                 0
rcb                              0
power_shot_power                 0
cf                               0
rf                               0
rw                               0
lam                              0
cam                              0
ram                              0
lm                               0
lcm                              0
cm                               0
rcm                              0
rm                               0
lwb                              0
ldm                              0
cdm                              0
rdm                              0
rwb                              0
lb                               0
lcb                              0
cb                               0
lf                               0
lw                               0
rs                               0
mentality_penalties              0
power_jumping                    0
power_stamina                    0
power_strength                   0
power_long_shots                 0
mentality_aggression             0
mentality_interceptions          0
mentality_positioning            0
mentality_vision                 0
defending_marking                0
st                               0
defending_standing_tackle        0
defending_sliding_tackle         0
goalkeeping_diving               0
goalkeeping_handling             0
goalkeeping_kicking              0
goalkeeping_positioning          0
goalkeeping_reflexes             0
ls                               0
short_name                       0
dtype: int64

Now all columns are filled with values, except for "player_traites".

Let's do some EDA¶

The aim is to see some interesting statistics about players and teams related to nationality, clubs, prices, age, etc.

First we will group the players by nationality and see which are the top 50 nationalities with largest number of players.

There will be many plottings, so in order to make our lives easy, we'd better make a function with appropriate parameters.

def draw_graphs(dataset,title,analyzed_feature,groupby_feature=None, stat_function=None,asc=None,orient=None,
               xtitle=None,ytitle=None,num_recs=None,marker_color="blue"):
    """
    Parameters and values they can take:
    stat_function="max","sum,"mean","count"
    orient="v","h"
       
    
    """
    if stat_function=="count":
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].count().sort_values(ascending=asc)[0:num_recs]
    elif stat_function=="max":
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].max().sort_values(ascending=asc)[0:num_recs]
    elif stat_function=="sum":
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].sum().sort_values(ascending=asc)[0:num_recs]
    else:
        graph_data=dataset.groupby(groupby_feature)[analyzed_feature].mean().sort_values(ascending=asc)[0:num_recs]
    if orient=='v':
        fig=px.bar(graph_data,x=graph_data.index,y=graph_data,orientation=orient,width=900, height=500)
    else:
        fig=px.bar(graph_data,x=graph_data,y=graph_data.index,orientation=orient,width=900, height=500)
    fig.update_layout(
    title_text=title,
    xaxis_title_text=xtitle,
    yaxis_title_text=ytitle) 
    fig.update_traces(marker_color=marker_color) 
    fig.show()

draw_graphs(dataset=players_16,title="Number of players per Nation (top 50)",
            analyzed_feature="nationality",groupby_feature="nationality",
            asc=False,xtitle="Nation",ytitle="Total number of players",
            stat_function="count",
            orient='v',num_recs=50,marker_color="violet")

Let's explore the age of players. We will take the top 20 most represented countries. We will calculate the average age per country and see some distributions.

top_20_nations=players_16.groupby("nationality")["nationality"].count().sort_values(ascending=False)[:20]

# Selecting all players which nationalities are in the top 20 most represented countries.

players_top_20_nations=players_16[players_16["nationality"].isin(top_20_nations.index)]

draw_graphs(dataset=players_top_20_nations,title="Average age of player per nationality",
            analyzed_feature="age",groupby_feature="nationality",
            asc=True,xtitle="Age",ytitle="Nationality",
            stat_function="mean",
            orient='h',num_recs=20)

The difference between lowest and highest age is about 2.5 years. Which is kind of expected. It is worth mentioning that youngest players come from England and the Netherlands which might be a sign of productive schools for young footballers.

nationality_and_age=players_top_20_nations[["nationality","age"]]
fig = px.box(nationality_and_age, x="nationality", y="age")

fig.show()

It is interesting to see who the outliers are.

nations_list=["England","Sweden", "Colombia","Mexico","Norway"]
outliers=pd.DataFrame()
for nation in nations_list:
    player_nationality=nation
    nationality_max_age=players_top_20_nations[players_top_20_nations["nationality"]==nation]["age"].max()
    outliers=outliers.append(players_top_20_nations[(players_top_20_nations["nationality"]==nation) & (players_top_20_nations["age"]==nationality_max_age)])# & players_top_20_nations["age"]==nationality_max_age])
outliers

Nothing interesting here since these are players in maybe some very low division clubs.

Which leads us to the idea of having these statistics made per nationality of the club players are in. Let's take the 5 best football leagues in Europe, those of: England, Spain, Italy, Germany and France. In this dataset we don't have information which league each club belongs to. So we have to get this data from another source.

The clubs dataset is created manually by copy/pasting lists with teams which participated in the number one league of each country (above mentioned five) during the 2015/16 season. These lists are taken from Wikipedia.

clubs_data=pd.read_csv("data/Main_leagues_2016.csv",encoding="utf-8",sep="\\t")

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:1: ParserWarning:

Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.

clubs_data

#Count the number of teams in each league

clubs_data.groupby("League")["League"].count()

League
Bundesliga        18
La Liga           20
Ligue 1           20
Premier League    20
Serie A           20
Name: League, dtype: int64

Data seems ok. But we have to check if the names of clubs coinside in both tables (most probably, not exactly). In list "missing" we will put those club names from clubs_data which are not found in players_16.

missing=[club for club in clubs_data["Club"].unique() if club not in (players_16["club"].unique())]

missing

['Málaga']

This is quite strange that "Malaga" is missing in the data base. The manual search also didn't give any result. We have to live with that. One thing which was nothiced is that one and the same club has two ways of naming: "Athletic Club de Bilbao" and "Bilbao Athletic". In general, this club is known as "Athletico Bilbao", so I will rename all occurrences.

players_16["club"]=players_16["club"].str.replace("Athletic Club de Bilbao","Athletico Bilbao")
players_16["club"]=players_16["club"].str.replace("Bilbao Athletic" ,"Athletico Bilbao")

Here we will check if the substitution went correctly. All clubs containing "Bilbao" should be only "Athletic Bilbao".

players_16[(players_16["club"].str.find("Bilbao"))!=-1]["club"].unique()

array(['Athletico Bilbao'], dtype=object)

Finally, we can merge the datasets and sametime make s sub dataset with players only from the major European leagues and see statistics for them.

euro_league_players=players_16.merge(clubs_data, left_on='club', right_on='Club')

euro_league_players.shape

(2714, 92)

euro_league_players.head()

draw_graphs(dataset=euro_league_players,title="Average age per league",
            analyzed_feature="age",groupby_feature="League",
            asc=False,xtitle="League",ytitle="Age",
            stat_function="mean",
            orient='v',marker_color="lightblue")

It is interesting to see that England was the country with youngest players (average age is: 23.78) but the average age in Premier League is quite high 25.21.

Below we will show boxplots of ages per league.

league_and_age=euro_league_players[["League","age"]]
fig = px.box(league_and_age, x="League", y="age",color="League",title="Age per League")

fig.show()

Let's check again the outliers because it is interesting to see who is playeing at the age of 42 and 40 in such serious tournaments. We will show the oldest footballer in each league.

#leagues_list=["England","Sweden", "Colombia","Mexico","Norway"]
euro_outliers=pd.DataFrame()
for league in euro_league_players["League"].unique():
    player_league=league
    league_max_age=euro_league_players[euro_league_players["League"]==league]["age"].max()
    euro_outliers=euro_outliers.append(euro_league_players[(euro_league_players["League"]==league) & (euro_league_players["age"]==league_max_age)])# & players_top_20_nations["age"]==nationality_max_age])
euro_outliers

$$$¶

And now let's finally see the money part. First we will check the major leagues, after that will make the analysis on the whole dataset.

euro_leagues_value=euro_league_players.groupby("League")["value_eur"].sum().sort_values()

draw_graphs(dataset=euro_league_players,title="Total value per league",
            analyzed_feature="value_eur",groupby_feature="League",
            asc=True,xtitle="Total Value",ytitle="League",
            stat_function="sum",
            orient='h',marker_color="lightgreen")

draw_graphs(dataset=euro_league_players,title="Average player value per league",
            analyzed_feature="value_eur",groupby_feature="League",
            asc=True,xtitle="Average Value",ytitle="Age",
            stat_function="mean",
            orient='h',marker_color="violet")

draw_graphs(dataset=euro_league_players,title="Total value per club",
            analyzed_feature="value_eur",groupby_feature="club",
            asc=False,xtitle="Club",ytitle="Total Value",
            stat_function="sum",
            orient='v',num_recs=20,marker_color="lightyellow")

draw_graphs(dataset=euro_league_players,title="Average value per club",
            analyzed_feature="value_eur",groupby_feature="club",
            asc=False,xtitle="Club",ytitle="Average Value",
            stat_function="mean",
            orient='v',num_recs=20,marker_color="orange")

draw_graphs(dataset=euro_league_players,title="Maximum player value per club",
            analyzed_feature="value_eur",groupby_feature="club",
            asc=False,xtitle="Club",ytitle="Maximum Value",
            stat_function="max",
            orient='v',num_recs=20,marker_color="rosybrown")

players_top_20_nations

draw_graphs(dataset=players_top_20_nations,title="Average player value per nationality",
            analyzed_feature="value_eur",groupby_feature="nationality",
            asc=True,xtitle="Value",ytitle="Nationality",
            stat_function="mean",
            orient='h',num_recs=20,marker_color="rebeccapurple")

Brazilian players have quite substantial prevailance in the price per player. Here interesting part is though the Netherlands have youngest players, their average value is quite high compared to other nationalities.

Now it is time to see where the top 300 most expensive players come from. This we will also visualize on a map. For this purpose we will import another dataset which contains the geographical coordinates of the capitals of countries. So, in this way we will position each player in his home country. Functions for spatial visualizations are taken from [3]

country_coords=pd.read_csv("data/gps_coords.csv")

country_coords.head()

country_coords.describe()

country_coords.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 245 entries, 0 to 244
Data columns (total 6 columns):
CountryName         245 non-null object
CapitalName         241 non-null object
CapitalLatitude     245 non-null float64
CapitalLongitude    245 non-null float64
CountryCode         242 non-null object
ContinentName       245 non-null object
dtypes: float64(2), object(4)
memory usage: 11.6+ KB

From this analysis we can see that there is no missing data. The dataset has column for country, so we can directly join both datasets and take the coordinates.

top_300_val_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[:300]

top_300_val_players=pd.DataFrame(top_300_val_players,index=None).reset_index()

top_300_val_players=pd.DataFrame(top_300_val_players)
top_300_val_players.merge(country_coords, left_on='nationality', right_on='CountryName')

Though we chose 300 players, in the resulting merged dataset we can see only 275 records. This might mean that some of the countries' name do not coinside in both tables. Let's explore.

top_300_val_players.nationality.unique()

array(['Argentina', 'Portugal', 'Belgium', 'Brazil', 'Uruguay',
       'Colombia', 'Germany', 'Netherlands', 'Wales', 'Poland', 'France',
       'Spain', 'Chile', 'Croatia', 'Sweden', 'England', 'Italy',
       'Austria', 'Bosnia Herzegovina', 'Denmark', 'Serbia', 'Turkey',
       'Switzerland', 'Slovakia', 'Ukraine', 'Ivory Coast', 'Mexico',
       'Gabon', 'Czech Republic', 'Japan', 'Morocco', 'Greece', 'Finland',
       'Algeria', 'Egypt', 'Montenegro', 'Armenia', 'Slovenia', 'Peru',
       'Cameroon', 'Russia', 'Venezuela', 'Republic of Ireland',
       'United States', 'Israel'], dtype=object)

country_coords.CountryName.unique()

array(['Somaliland', 'South Georgia and South Sandwich Islands',
       'French Southern and Antarctic Lands', 'Palestine',
       'Aland Islands', 'Nauru', 'Saint Martin', 'Tokelau',
       'Western Sahara', 'Afghanistan', 'Albania', 'Algeria',
       'American Samoa', 'Andorra', 'Angola', 'Anguilla',
       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Aruba',
       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
       'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',
       'Bermuda', 'Bhutan', 'Bolivia', 'Bosnia and Herzegovina',
       'Botswana', 'Brazil', 'British Virgin Islands',
       'Brunei Darussalam', 'Bulgaria', 'Burkina Faso', 'Myanmar',
       'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',
       'Cayman Islands', 'Central African Republic', 'Chad', 'Chile',
       'China', 'Christmas Island', 'Cocos Islands', 'Colombia',
       'Comoros', 'Democratic Republic of the Congo', 'Republic of Congo',
       'Cook Islands', 'Costa Rica', "Cote d'Ivoire", 'Croatia', 'Cuba',
       'Curaçao', 'Cyprus', 'Czech Republic', 'Denmark', 'Djibouti',
       'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',
       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Estonia',
       'Ethiopia', 'Falkland Islands', 'Faroe Islands', 'Fiji', 'Finland',
       'France', 'French Polynesia', 'Gabon', 'The Gambia', 'Georgia',
       'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada',
       'Guam', 'Guatemala', 'Guernsey', 'Guinea', 'Guinea-Bissau',
       'Guyana', 'Haiti', 'Vatican City', 'Honduras', 'Hungary',
       'Iceland', 'India', 'Indonesia', 'Iran', 'Iraq', 'Ireland',
       'Isle of Man', 'Israel', 'Italy', 'Jamaica', 'Japan', 'Jersey',
       'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati', 'North Korea',
       'South Korea', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',
       'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein',
       'Lithuania', 'Luxembourg', 'Macedonia', 'Madagascar', 'Malawi',
       'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',
       'Mauritania', 'Mauritius', 'Mexico',
       'Federated States of Micronesia', 'Moldova', 'Monaco', 'Mongolia',
       'Montenegro', 'Montserrat', 'Morocco', 'Mozambique', 'Namibia',
       'Nepal', 'Netherlands', 'New Caledonia', 'New Zealand',
       'Nicaragua', 'Niger', 'Nigeria', 'Niue', 'Norfolk Island',
       'Northern Mariana Islands', 'Norway', 'Oman', 'Pakistan', 'Palau',
       'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines',
       'Pitcairn Islands', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',
       'Romania', 'Russia', 'Rwanda', 'Saint Barthelemy', 'Saint Helena',
       'Saint Kitts and Nevis', 'Saint Lucia',
       'Saint Pierre and Miquelon', 'Saint Vincent and the Grenadines',
       'Samoa', 'San Marino', 'Sao Tome and Principe', 'Saudi Arabia',
       'Senegal', 'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',
       'Sint Maarten', 'Slovakia', 'Slovenia', 'Solomon Islands',
       'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka',
       'Sudan', 'Suriname', 'Svalbard', 'Swaziland', 'Sweden',
       'Switzerland', 'Syria', 'Taiwan', 'Tajikistan', 'Tanzania',
       'Thailand', 'Timor-Leste', 'Togo', 'Tonga', 'Trinidad and Tobago',
       'Tunisia', 'Turkey', 'Turkmenistan', 'Turks and Caicos Islands',
       'Tuvalu', 'Uganda', 'Ukraine', 'United Arab Emirates',
       'United Kingdom', 'United States', 'Uruguay', 'Uzbekistan',
       'Vanuatu', 'Venezuela', 'Vietnam', 'US Virgin Islands',
       'Wallis and Futuna', 'Yemen', 'Zambia', 'Zimbabwe',
       'US Minor Outlying Islands', 'Antarctica', 'Northern Cyprus',
       'Hong Kong', 'Heard Island and McDonald Islands',
       'British Indian Ocean Territory', 'Macau'], dtype=object)

top_300_set=set(top_300_val_players.nationality.unique())
country_set=set(country_coords.CountryName.unique())

top_300_set.issubset(country_set)

False

False shows that our suspicion for not coinciding names is true and by subtracting two sets we can identify the differences.

top_300_set-country_set

{'Bosnia Herzegovina',
 'England',
 'Ivory Coast',
 'Republic of Ireland',
 'Wales'}

Let's see how these countries' names are spelt in the country_set. And here is the difference: "Bosnia Herzegovina" is instead of 'Bosnia and Herzegovina' and "Cote d'Ivoire" stands for "Ivory coast". England and Wales do not exist in the database as separate countries, which is logical. Let's change the names of "Bosnia and Herzegovina", "Cote d'Ivoire" and "Ireland" (in the dataset the coordinates are thos of Dublin, so we should change it to Republic of Ireland). For England, Northern Ireland and Wales we will add records by using a dictionary.

country_coords.CountryName=country_coords.CountryName.str.replace("Bosnia and Herzegovina","Bosnia Herzegovina")
country_coords.CountryName=country_coords.CountryName.str.replace("Cote d'Ivoire","Ivory Coast")
country_coords.CountryName=country_coords.CountryName.str.replace("Ireland","Republic of Ireland")

coords_dict={"eng":{"CountryName":"England",
                    "CapitalName":"London",
                    "CapitalLatitude":"51.500000",
                    "CapitalLongitude":"-0.083333",
                    "CountryCode":"NaN",
                    "ContinentName":"Europe"},
             "wal":{"CountryName":"Wales",
                    "CapitalName":"Cardiff",
                    "CapitalLatitude":"51.481583",
                    "CapitalLongitude":"-3.179090",
                    "CountryCode":"NaN",
                    "ContinentName":"Europe"},
            "irl":{"CountryName":"Northern Ireland",
                    "CapitalName":"Belfast",
                    "CapitalLatitude":" 54.607868",
                    "CapitalLongitude":"-5.926437",
                    "CountryCode":"NaN",
                    "ContinentName":"Europe"}}

for key,value in coords_dict.items():
    country_coords=country_coords.append(coords_dict[key],ignore_index=True)

Now, let's do the merge again.

players_coordinates=top_300_val_players.merge(country_coords, left_on='nationality', right_on='CountryName')

players_coordinates.shape

(300, 9)

This time the records are exactly 300.

players_coordinates.CapitalLongitude=players_coordinates.CapitalLongitude.astype(float)
players_coordinates.CapitalLatitude=players_coordinates.CapitalLatitude.astype(float)

plt.figure(figsize = (12, 10))
m = Basemap(projection = "merc", llcrnrlat = -73, llcrnrlon = -180, urcrnrlat = 80, urcrnrlon = 180)
x, y = None, None
x, y = m(players_coordinates.CapitalLongitude.tolist(),players_coordinates.CapitalLatitude.tolist())
m.plot(x,y,"o",color="red",markersize=2)
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color = "lightgreen", lake_color = "aqua")
m.drawmapboundary(fill_color = "aqua")
plt.show()

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:2: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:7: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

def plot_players_densities(coord_data, title = "Players densities"):
    plt.figure(figsize = (15, 10))
    m = Basemap(projection = "merc", llcrnrlat = -73, llcrnrlon = -180, urcrnrlat = 80, urcrnrlon = 180)

    # Prepare histogram bins
    num_bins = 100
    lon_bins = np.linspace(-180, 180, num_bins + 1)
    lat_bins = np.linspace(-90, 90, num_bins + 1)

    # Create 2D histogram values
    density, x_breaks, y_breaks = np.histogram2d(
        coord_data.CapitalLatitude,
        coord_data.CapitalLongitude, 
        [lat_bins, lon_bins])

    # Create the basis of the histogram - the (x, y) value pairs
    # and map them to 2D distances
    lon_bins_2d, lat_bins_2d = np.meshgrid(lon_bins, lat_bins)
    x, y = m(lon_bins_2d, lat_bins_2d)

    m.drawcoastlines()
    m.drawcountries()
    m.pcolormesh(x, y, density)
    m.colorbar()
    plt.title(title)
    plt.show()

plot_players_densities(players_coordinates)

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:3: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

C:\Users\mbararova\AppData\Local\Continuum\anaconda3\lib\site-packages\ipykernel_launcher.py:22: MatplotlibDeprecationWarning:


The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.

Most of the 300 highest paid professionals are from Spain. We will investigate little bit by checking what si their number in the second group of 300 players.

second_300_val_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[301:600]

second_300_val_players=pd.DataFrame(second_300_val_players,index=None).reset_index()

second_300_val_players.groupby("nationality")["nationality"].count().sort_values(ascending=False).head(10)

nationality
Spain          43
France         25
Brazil         23
Italy          20
England        19
Germany        16
Argentina      14
Portugal       14
Netherlands    11
Turkey         10
Name: nationality, dtype: int64

Again we can see that spanish players are the biggest number in this group. Though the country with highest number of representatives in the database is England (about 47% more).

And now let's see the lower range players. Will take players from 8001-st to 8300-th in the descending list of player values.

lower_paid_players=players_16.groupby(["short_name","nationality"])["value_eur"].sum().sort_values(ascending=False)[8001:8300]
lower_paid_players=pd.DataFrame(lower_paid_players,index=None).reset_index()
lower_paid_players.groupby("nationality")["nationality"].count().sort_values(ascending=False).head(10)

nationality
England          23
France           21
Argentina        20
United States    18
Spain            15
Italy            15
Chile            15
Germany          12
Poland           11
Colombia         11
Name: nationality, dtype: int64

Here we can see that Spaniards are not largest in number.

From these last few statistics it seems that, in general, Spanish players are better paid than others, though the average price per player is not highest. Still, number of players in the set is very high and this can be the reason for this result.

nationality_and_value=players_top_20_nations[["nationality","value_eur"]]
fig = px.box(nationality_and_value, x="nationality", y="value_eur",title="Value per Nation")

fig.show()

players_top_20_nations[players_top_20_nations["nationality"]=="Spain"]["value_eur"].median()

875000.0

players_top_20_nations[players_top_20_nations["nationality"]=="England"]["value_eur"].median()

300000.0

Seeing the median values for Brazil(1.8M), Portugal(1.2M), Spain(875000) and England(300000) and having in mind the number of players from Spain (987), England (1450), Brazil(420) and Portugal(324), we can only conclude that Spanish players are much better paid than English ones. Brazil and Portugal are less in numbers and each have 1 very expensive player which rizes the average value per nationality.

Lets make a hypothesis:

$H0$ - there is not a substantial difference in the payment of English and Spanish players

$H1$ - there is a substantial difference in the payment of English and Spanish players

and we will use $\alpha_{c}$=1% since we have big dataset.

This we will test now and verify with the second dataset for 2020.

english_players_price=players_16[players_16["nationality"]=="England"]["value_eur"]
spanish_players_price=players_16[players_16["nationality"]=="Spain"]["value_eur"]

ttest_ind(english_players_price, spanish_players_price)

Ttest_indResult(statistic=-11.46332634242886, pvalue=1.1461395477178186e-29)

With such a low pvalue we can reject the Null Hypothesis.

The final aim of this work is to create and train a model to predict the price of a player based on his capabilities. Before that we can see which features are most correlated to the value.

players_16.corr()

From the correlation we can see that maximum correlation the value has with international reputation and overall rating of the player. Which is logical.

The Model¶

Linear Regression¶

Linear Regression model is used when we need to predict a continuous variable using a linear function of type $y=ax+b$. $a$ and $b$ are unknown parameters. Given pairs $(x,y)$ we create a model under the assumption that $y$ depends linearly on $x$. [4]

Below is the representation of data and its linear regression model taken from Wikipedia.

LinRegr

The red line is the approximation of the data and for one independednt variable $x$, the dependent variable $y$ will look like: $y=\theta_0+\theta_1*x_1$ (or $b+ax$). If we have more independent variables (called features) $x_1,x_2,...,x_n$ then $y=\theta_0+\theta_1*x_1+\theta_2*x_2+...+\theta_n*x_n$.

Here we will create a model with 78 features, which after converting categorical variables to numerical will become 114.

players_16.head().transpose()

Before submitting the dataset to any ML algorithm, we need to do some more preprocessing. Column "dob" (date of birth) can be removed since column "age" is there and they both give same information. Names of players should be removed, as well as "players_traits", "joined", "body_type", "real_face" and "contract_valid_until". These are hardly supposed to influence the value of the player. "players_positions" can also be removed since the values in the columns with the respective positions depict players capabilities in each position. Naturally, they will have higher values for the positions the player takes.

players_16=players_16.drop(["short_name","dob","player_traits", "joined", "body_type", "real_face","contract_valid_until","nationality","player_positions","team_jersey_number","club"],axis=1)

players_16.head().transpose()

We will rearrange columns little bit by putting the target attribute at the beginning of the set so we can easily remove it later.

players_16.columns

Index(['age', 'height_cm', 'weight_kg', 'overall', 'potential', 'value_eur',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb'],
      dtype='object')

players_16=players_16[['value_eur','age', 'height_cm', 'weight_kg', 'overall', 'potential',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']]

players_16.head()

players_16.shape

(14881, 78)

We are going to apply Linear Regression in order to train a model to predict players' price. scikit-learn will be used. There are few steps before that.

Converting categorical values to numerical
Normalization
Splitting the data

Converting categorical values to numerical¶

ML algorithms cannot work with categorical features, that's why they have to be converted to numerical. One way is by encoding (substituting) each value of a categorical feature by a binary number with n digits (actually n-1), where n is the number of unique values of the feature.

dummy_features_foot=pd.get_dummies(players_16["preferred_foot"])
dummy_features_foot.columns

Index(['Left', 'Right'], dtype='object')

dummy_features_position=pd.get_dummies(players_16["team_position"])
dummy_features_position.columns

Index(['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
       'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
       'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB'],
      dtype='object')

dummy_features_rate=pd.get_dummies(players_16["work_rate"])
dummy_features_rate.columns

Index(['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium'],
      dtype='object')

players_16=players_16.drop(["preferred_foot","team_position","work_rate"],axis=1)

players_16[['Left', 'Right']]=dummy_features_foot
players_16[['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
       'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
       'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB']]=dummy_features_position
players_16[['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium']]=dummy_features_rate

players_16.head()

modelling_data_features = players_16.drop("value_eur", axis = 1)
modelling_data_target = players_16["value_eur"]

modelling_data_features.columns

Index(['age', 'height_cm', 'weight_kg', 'overall', 'potential',
       'international_reputation', 'weak_foot', 'skill_moves', 'pace',
       'shooting',
       ...
       'SUB', 'High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium'],
      dtype='object', length=114)

Normalization¶

In order to have better accuracy of the model, the values of all features should have same scale.

scaler = MinMaxScaler()
modelling_data_features_scaled=scaler.fit_transform(modelling_data_features)

Splitting the data¶

features_train, features_test, target_train, target_test = train_test_split(
    modelling_data_features_scaled, modelling_data_target, train_size = 0.7, test_size = 0.3, random_state = 42)
print(features_train.shape, features_test.shape, target_train.shape, target_test.shape, sep = "\r\n")

(10416, 114)
(4465, 114)
(10416,)
(4465,)

Run the Model¶

model = LinearRegression()
model.fit(features_train, target_train)

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)

score=model.score(features_test,target_test)
score

0.6273786112802693

Check the model with dataset from a different year (2020)¶

players_20=pd.read_csv("data/players_20.csv",index_col="sofifa_id")

players_20.head()

The new dataset has same format as the old one, so we will do the same steps for data cleaning.

players_20_for_modelling=players_20.drop(["player_url","long_name","wage_eur","release_clause_eur","mentality_composure", "nation_position", "nation_jersey_number", "loaned_from","player_tags"],axis=1)

players_20_for_modelling=players_20_for_modelling.drop(["gk_diving","gk_handling", "gk_kicking","gk_reflexes","gk_positioning"],axis=1)

cols_for_fill_20=['pace',
       'shooting', 'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing',
       'attacking_finishing', 'attacking_heading_accuracy',
       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',
       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',
       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',
       'movement_agility', 'movement_reactions', 'movement_balance',
       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',
       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']

transform_columns_to_number(players_20_for_modelling,cols_for_fill_20)

players_20_for_modelling.isnull().sum().sort_values(ascending=False)

player_traits                 10712
joined                         1288
contract_valid_until            240
team_jersey_number              240
team_position                   240
attacking_finishing               0
dribbling                         0
defending                         0
physic                            0
gk_speed                          0
attacking_crossing                0
attacking_heading_accuracy        0
shooting                          0
attacking_short_passing           0
attacking_volleys                 0
skill_dribbling                   0
skill_curve                       0
skill_fk_accuracy                 0
skill_long_passing                0
skill_ball_control                0
movement_acceleration             0
movement_sprint_speed             0
passing                           0
rb                                0
pace                              0
movement_reactions                0
age                               0
dob                               0
height_cm                         0
weight_kg                         0
nationality                       0
club                              0
overall                           0
potential                         0
value_eur                         0
player_positions                  0
preferred_foot                    0
international_reputation          0
weak_foot                         0
skill_moves                       0
work_rate                         0
body_type                         0
real_face                         0
movement_agility                  0
movement_balance                  0
rcb                               0
power_shot_power                  0
cf                                0
rf                                0
rw                                0
lam                               0
cam                               0
ram                               0
lm                                0
lcm                               0
cm                                0
rcm                               0
rm                                0
lwb                               0
ldm                               0
cdm                               0
rdm                               0
rwb                               0
lb                                0
lcb                               0
cb                                0
lf                                0
lw                                0
rs                                0
mentality_penalties               0
power_jumping                     0
power_stamina                     0
power_strength                    0
power_long_shots                  0
mentality_aggression              0
mentality_interceptions           0
mentality_positioning             0
mentality_vision                  0
defending_marking                 0
st                                0
defending_standing_tackle         0
defending_sliding_tackle          0
goalkeeping_diving                0
goalkeeping_handling              0
goalkeeping_kicking               0
goalkeeping_positioning           0
goalkeeping_reflexes              0
ls                                0
short_name                        0
dtype: int64

columns_20=["joined","team_position","contract_valid_until"]
fill_missing_data(players_20_for_modelling,columns)

Test the model and check the hypothesis with new data¶

players_20_for_modelling["team_jersey_number"]=players_20_for_modelling["team_jersey_number"].fillna(0)

players_20_for_modelling.isnull().sum().sort_values(ascending=False)

player_traits                 10712
rb                                0
movement_reactions                0
shooting                          0
passing                           0
dribbling                         0
defending                         0
physic                            0
gk_speed                          0
attacking_crossing                0
attacking_finishing               0
attacking_heading_accuracy        0
attacking_short_passing           0
attacking_volleys                 0
skill_dribbling                   0
skill_curve                       0
skill_fk_accuracy                 0
skill_long_passing                0
skill_ball_control                0
movement_acceleration             0
movement_sprint_speed             0
pace                              0
contract_valid_until              0
joined                            0
value_eur                         0
age                               0
dob                               0
height_cm                         0
weight_kg                         0
nationality                       0
club                              0
overall                           0
potential                         0
player_positions                  0
team_jersey_number                0
preferred_foot                    0
international_reputation          0
weak_foot                         0
skill_moves                       0
work_rate                         0
body_type                         0
real_face                         0
team_position                     0
movement_agility                  0
movement_balance                  0
rcb                               0
power_shot_power                  0
cf                                0
rf                                0
rw                                0
lam                               0
cam                               0
ram                               0
lm                                0
lcm                               0
cm                                0
rcm                               0
rm                                0
lwb                               0
ldm                               0
cdm                               0
rdm                               0
rwb                               0
lb                                0
lcb                               0
cb                                0
lf                                0
lw                                0
rs                                0
mentality_penalties               0
power_jumping                     0
power_stamina                     0
power_strength                    0
power_long_shots                  0
mentality_aggression              0
mentality_interceptions           0
mentality_positioning             0
mentality_vision                  0
defending_marking                 0
st                                0
defending_standing_tackle         0
defending_sliding_tackle          0
goalkeeping_diving                0
goalkeeping_handling              0
goalkeeping_kicking               0
goalkeeping_positioning           0
goalkeeping_reflexes              0
ls                                0
short_name                        0
dtype: int64

players_20_for_modelling=players_20_for_modelling.drop(["short_name","dob","player_traits", "joined", "body_type", "real_face","contract_valid_until","player_positions","nationality","team_jersey_number","club"],axis=1)

players_20_for_modelling=players_20_for_modelling[['value_eur','age', 'height_cm', 'weight_kg', 'overall', 'potential',
       'preferred_foot', 'international_reputation', 'weak_foot',
       'skill_moves', 'work_rate', 'team_position', 'pace', 'shooting',
       'passing', 'dribbling', 'defending', 'physic', 'gk_speed',
       'attacking_crossing', 'attacking_finishing',
       'attacking_heading_accuracy', 'attacking_short_passing',
       'attacking_volleys', 'skill_dribbling', 'skill_curve',
       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',
       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',
       'movement_reactions', 'movement_balance', 'power_shot_power',
       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',
       'mentality_aggression', 'mentality_interceptions',
       'mentality_positioning', 'mentality_vision', 'mentality_penalties',
       'defending_marking', 'defending_standing_tackle',
       'defending_sliding_tackle', 'goalkeeping_diving',
       'goalkeeping_handling', 'goalkeeping_kicking',
       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',
       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',
       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',
       'rcb', 'rb']]

dummy_features_foot_20=pd.get_dummies(players_20_for_modelling["preferred_foot"])
dummy_features_position_20=pd.get_dummies(players_20_for_modelling["team_position"])
dummy_features_rate_20=pd.get_dummies(players_20_for_modelling["work_rate"])

players_20_for_modelling=players_20_for_modelling.drop(["preferred_foot","team_position","work_rate"],axis=1)

players_20_for_modelling[['Left', 'Right']]=dummy_features_foot_20
players_20_for_modelling[['CAM', 'CB', 'CDM', 'CF', 'CM', 'GK', 'LAM', 'LB', 'LCB', 'LCM', 'LDM',
       'LF', 'LM', 'LS', 'LW', 'LWB', 'RAM', 'RB', 'RCB', 'RCM', 'RDM', 'RES',
       'RF', 'RM', 'RS', 'RW', 'RWB', 'ST', 'SUB']]=dummy_features_position_20
players_20_for_modelling[['High/High', 'High/Low', 'High/Medium', 'Low/High', 'Low/Low',
       'Low/Medium', 'Medium/High', 'Medium/Low', 'Medium/Medium']]=dummy_features_rate_20

modelling_data_20=pd.get_dummies(players_20_for_modelling)

modelling_data_features_20 = modelling_data_20.drop("value_eur", axis = 1)
modelling_data_target_20 = modelling_data_20["value_eur"]

scaler = MinMaxScaler()
modelling_data_features_scaled_20=scaler.fit_transform(modelling_data_features_20)

score=model.score(modelling_data_features_scaled_20,modelling_data_target_20 )
score

0.5364270009269339

This is very low accuracy. Either data is not properly preprocessed, or the available data has not much relation to the price of players. Main features that influence value might not be present in the data.

Checking the Hypothesis¶

english_players_price_20=players_20[players_20["nationality"]=="England"]["value_eur"]
spanish_players_price_20=players_20[players_20["nationality"]=="Spain"]["value_eur"]

ttest_ind(english_players_price_20, spanish_players_price_20)

Ttest_indResult(statistic=-12.978089447519524, pvalue=2.0915163608115403e-37)

Here the pvalue is even smaller than for 2016, which again leads to rejecting the H0 hypothesis. All this means that Spanish and English players do not have equal payment.

Future work¶

Many more analysis can be done with this dataset. Players_traits can be investigated. Do they have any influence on the value? Individual players progress through the years can be also checked (the complete set contains data for all years 2015 - 2020).

References¶

[1] Wikipedia
[2] https://www.guidetofootball.com/tactics/playing-positions/
[3] Yordan Darakchiev. SoftUni "Data Science" course, Working-with-Spatial-Data-and-Network-Analysis, 2020.
[4] Yordan Darakchiev. SoftUni "Data Science" course, Regression Models, 2020.
[5] Jake VanderPlas. Python Data Science Handbook. O'Reilly, 2017.

	count	mean	std	min	25%	50%	75%	max
age	14881.0	2.493045e+01	4.603521e+00	16.0	21.00	24.0	28.00	45.0
height_cm	14881.0	1.811025e+02	6.701927e+00	155.0	176.00	181.0	186.00	204.0
weight_kg	14881.0	7.542820e+01	6.933863e+00	50.0	70.00	75.0	80.00	110.0
overall	14881.0	6.565043e+01	7.095894e+00	44.0	61.00	66.0	70.00	94.0
potential	14881.0	7.028869e+01	6.293664e+00	44.0	66.00	70.0	75.00	95.0
value_eur	14881.0	1.766313e+06	4.172073e+06	0.0	250000.00	575000.0	1400000.00	111000000.0
international_reputation	14881.0	1.119145e+00	3.972144e-01	1.0	1.00	1.0	1.00	5.0
weak_foot	14881.0	2.942477e+00	6.580131e-01	1.0	3.00	3.0	3.00	5.0
skill_moves	14881.0	2.290572e+00	7.293560e-01	1.0	2.00	2.0	3.00	5.0
team_jersey_number	14710.0	1.942536e+01	1.619540e+01	1.0	8.00	17.0	26.00	99.0
contract_valid_until	14710.0	2.017491e+03	1.734312e+00	2015.0	2016.00	2017.0	2019.00	2021.0
pace	13263.0	6.843550e+01	1.102372e+01	21.0	62.00	69.0	76.00	96.0
shooting	13263.0	5.260288e+01	1.396492e+01	14.0	42.00	55.0	63.00	93.0
passing	13263.0	5.707110e+01	1.083942e+01	20.0	50.00	58.0	65.00	93.0
dribbling	13263.0	6.194745e+01	1.075474e+01	22.0	56.00	63.0	69.00	95.0
defending	13263.0	5.099578e+01	1.767604e+01	15.0	34.00	56.0	65.00	90.0
physic	13263.0	6.530287e+01	9.631893e+00	27.0	59.00	66.0	72.00	89.0
gk_diving	1618.0	6.635600e+01	7.691925e+00	36.0	61.00	66.0	71.00	88.0
gk_handling	1618.0	6.244438e+01	8.300535e+00	42.0	56.25	63.0	68.00	87.0
gk_kicking	1618.0	6.101978e+01	8.286959e+00	29.0	55.00	60.0	66.75	91.0
gk_reflexes	1618.0	6.768232e+01	8.193498e+00	32.0	62.00	67.0	73.00	90.0
gk_speed	1618.0	4.375464e+01	8.375229e+00	12.0	39.00	44.0	48.00	64.0
gk_positioning	1618.0	6.318665e+01	8.869550e+00	37.0	57.00	63.0	69.00	90.0

	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
sofifa_id
200197	49+1	74+1	74+1	67+1	75+1	70+1	55+1	54+1	63+1	73+1	66	64	67	73+1	63	71+1	73+4	70	67	71+1	47+1	41+1	76+1	43+1	72+1	33+1	30+1	27+1	13+1	10+1	15+1	15+1	15+1	72+0	72+0	72+0	67+0	69+0	69+0	69+0	67+0	67+0	67+0	67+0	66+0	63+0	63+0	63+0	66+0	53+0	53+0	53+0	53+0	53+0	51+0	49+0	49+0	49+0	51+0
158663	66+2	66-3	53+1	66-3	68+1	69-1	56+1	61+1	65+1	68-3	85	84	71+2	66-1	73+1	67+1	66	72	65-2	67-1	61+1	52-8	62	58	69+1	62	61-3	59	6+1	13+1	16+1	6+1	9+1	66+0	66+0	66+0	69+0	67+0	67+0	67+0	69+0	67+0	67+0	67+0	68+0	65+0	65+0	65+0	68+0	66+0	63+0	63+0	63+0	66+0	65+0	61+0	61+0	61+0	65+0
201417	64+1	45+1	61+1	65+1	29+1	65+1	47+1	38+1	64+1	66+1	68-3	72	62	64+1	64	53+1	80	70	74+1	35+1	70+1	64	52+1	58+1	48+1	67+4	69+3	69+4	15+1	8+1	12+1	11+1	9+1	57+0	57+0	57+0	61+0	59+0	59+0	59+0	61+0	60+0	60+0	60+0	63+0	62+0	62+0	62+0	63+0	67+0	66+0	66+0	66+0	67+0	67+0	68+0	68+0	68+0	67+0
205198	67+5	53+21	54+1	63+3	32+1	67+3	55+21	41+1	61+1	54+9	67-1	66-1	74+20	47-16	76	34+1	70+15	58	47	52+20	63+1	55+11	56+2	45+1	42+1	52+1	51+4	46+1	12+1	9+1	15+1	10+1	10+1	53+0	53+0	53+0	59+0	56+0	56+0	56+0	59+0	57+0	57+0	57+0	59+0	56+0	56+0	56+0	59+0	57+0	55+0	55+0	55+0	57+0	56+0	53+0	53+0	53+0	56+0
213423	60-3	57+1	32+1	62-1	61-2	59+1	49+1	42+1	53+1	62+1	82+9	83+5	68	49+1	71	58+1	53	76+8	75+10	55+1	50+1	50+1	53+1	49+1	50+1	59+1	62+1	51+1	13+1	7+1	13+1	12+1	9+1	58+0	58+0	58+0	61+0	59+0	59+0	59+0	61+0	59+0	59+0	59+0	61+0	57+0	57+0	57+0	61+0	61+0	58+0	58+0	58+0	61+0	60+0	56+0	56+0	56+0	60+0
219120	14	12	15	32	9	19	12	11	27	22	44	45	35	48	46	20	58	27	60	11	27	16	14	43	13	18	20	15	60	58	56	60	62	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
207513	53+1	52+1	47+1	63+1	51+1	60+1	43+1	58+1	58+1	58+1	65	68	57	54+1	74+1	55+1	75+4	59	49	54+1	60+1	54+1	53+1	51+1	55+1	51+1	54+1	56+1	15+1	14+1	6+1	8+1	15+1	55+0	55+0	55+0	57+0	57+0	57+0	57+0	57+0	57+0	57+0	57+0	58+0	57+0	57+0	57+0	58+0	57+0	56+0	56+0	56+0	57+0	57+0	55+0	55+0	55+0	57+0

	CountryName	CapitalName	CapitalLatitude	CapitalLongitude	CountryCode	ContinentName
0	Somaliland	Hargeisa	9.550000	44.050000	NaN	Africa
1	South Georgia and South Sandwich Islands	King Edward Point	-54.283333	-36.500000	GS	Antarctica
2	French Southern and Antarctic Lands	Port-aux-Français	-49.350000	70.216667	TF	Antarctica
3	Palestine	Jerusalem	31.766667	35.233333	PS	Asia
4	Aland Islands	Mariehamn	60.116667	19.900000	AX	Europe

	CapitalLatitude	CapitalLongitude
count	245.000000	245.000000
mean	17.335901	12.905790
std	25.739778	72.941443
min	-54.283333	-175.200000
25%	0.316667	-36.500000
50%	16.700000	15.300000
75%	38.883333	45.333333
max	78.216667	179.216667

	age	height_cm	weight_kg	overall	potential	value_eur	international_reputation	weak_foot	skill_moves	team_jersey_number	contract_valid_until	pace	shooting	passing	dribbling	defending	physic	gk_speed	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
age	1.000000	0.080412	0.218434	0.439736	-0.125627	0.079883	0.277710	0.077216	-0.018800	-0.228214	-0.151552	-0.164913	0.048660	0.062909	-0.026930	0.100032	0.069155	0.086077	0.062796	0.005349	0.080368	0.086728	0.061369	-0.051007	0.070079	0.122131	0.139071	0.029144	-0.224824	-0.218302	-0.073392	0.395858	-0.123581	0.088910	0.121634	0.023215	0.259827	0.076456	0.237996	0.167387	0.014522	0.160532	0.071897	0.095487	0.085014	0.068744	0.110753	0.118913	0.114074	0.126850	0.109512	0.022931	0.022931	0.022931	-0.003591	0.009109	0.009109	0.009109	-0.003591	0.013235	0.013235	0.013235	0.001210	0.050825	0.050825	0.050825	0.001210	0.046002	0.084582	0.084582	0.084582	0.046002	0.049920	0.091531	0.091531	0.091531	0.049920
height_cm	0.080412	1.000000	0.766455	0.060498	0.017680	0.015859	0.045723	-0.164617	-0.413659	-0.043021	-0.094536	-0.478165	-0.378375	-0.425457	-0.481723	-0.094135	-0.137255	0.337328	-0.458348	-0.358111	-0.028599	-0.341943	-0.344828	-0.475107	-0.421572	-0.384103	-0.319234	-0.394236	-0.515279	-0.435221	-0.599136	0.003806	-0.787934	-0.281572	-0.073728	-0.259871	0.531675	-0.379441	-0.045248	-0.038329	-0.414091	-0.307677	-0.320547	-0.044097	-0.052524	-0.071170	0.345080	0.344673	0.340714	0.346370	0.343593	-0.371797	-0.371797	-0.371797	-0.455301	-0.426990	-0.426990	-0.426990	-0.455301	-0.438802	-0.438802	-0.438802	-0.445813	-0.391435	-0.391435	-0.391435	-0.445813	-0.325603	-0.258504	-0.258504	-0.258504	-0.325603	-0.285420	-0.152169	-0.152169	-0.152169	-0.285420
weight_kg	0.218434	0.766455	1.000000	0.134976	0.007411	0.042073	0.093912	-0.129611	-0.363400	-0.084685	-0.078132	-0.451028	-0.320896	-0.379390	-0.434535	-0.094449	-0.100458	0.323379	-0.405305	-0.312001	-0.033478	-0.302296	-0.291449	-0.431251	-0.368087	-0.326886	-0.279506	-0.353959	-0.484357	-0.413137	-0.546209	0.071590	-0.697291	-0.220455	-0.024147	-0.231913	0.595008	-0.316240	-0.002919	-0.035367	-0.360297	-0.253481	-0.265101	-0.051303	-0.063893	-0.081469	0.337671	0.339249	0.334518	0.340144	0.334678	-0.325837	-0.325837	-0.325837	-0.407746	-0.379890	-0.379890	-0.379890	-0.407746	-0.392627	-0.392627	-0.392627	-0.400793	-0.350573	-0.350573	-0.350573	-0.400793	-0.298411	-0.233983	-0.233983	-0.233983	-0.298411	-0.264230	-0.137809	-0.137809	-0.137809	-0.264230
overall	0.439736	0.060498	0.134976	1.000000	0.781817	0.598158	0.491618	0.205962	0.243675	-0.179027	-0.013182	0.144062	0.314795	0.356585	0.300196	0.250692	0.251938	-0.050719	0.337289	0.258967	0.285335	0.462311	0.313773	0.301068	0.354916	0.329594	0.449014	0.406070	0.157273	0.176556	0.211860	0.792814	0.037695	0.376679	0.237238	0.303921	0.292941	0.341751	0.391474	0.306587	0.286826	0.454645	0.288142	0.199397	0.224607	0.191321	-0.013305	-0.008285	-0.011916	-0.004463	-0.013170	0.321237	0.321237	0.321237	0.314071	0.324193	0.324193	0.324193	0.314071	0.328978	0.328978	0.328978	0.319067	0.355663	0.355663	0.355663	0.319067	0.314825	0.331386	0.331386	0.331386	0.314825	0.302586	0.287742	0.287742	0.287742	0.302586
potential	-0.125627	0.017680	0.007411	0.781817	1.000000	0.576793	0.380552	0.178361	0.255135	-0.022922	0.085902	0.213147	0.282428	0.314101	0.308350	0.177525	0.182002	-0.080463	0.289771	0.255918	0.225003	0.419149	0.279155	0.328817	0.316465	0.261308	0.374957	0.391883	0.265961	0.278954	0.246533	0.590323	0.109251	0.322904	0.160963	0.246595	0.128617	0.296538	0.241544	0.199210	0.272169	0.380165	0.251420	0.129611	0.162444	0.139049	-0.054610	-0.052694	-0.054882	-0.053324	-0.053684	0.297491	0.297491	0.297491	0.306260	0.310361	0.310361	0.310361	0.306260	0.313618	0.313618	0.313618	0.306790	0.317066	0.317066	0.317066	0.306790	0.270101	0.267885	0.267885	0.267885	0.270101	0.255058	0.217449	0.217449	0.217449	0.255058
value_eur	0.079883	0.015859	0.042073	0.598158	0.576793	1.000000	0.676484	0.140724	0.217407	-0.065684	0.092588	0.106889	0.212288	0.214805	0.194875	0.086131	0.107833	-0.015566	0.195654	0.194837	0.135334	0.272554	0.229246	0.204294	0.226008	0.204509	0.253297	0.245157	0.152016	0.153989	0.159247	0.471799	0.065007	0.217239	0.110937	0.164818	0.101879	0.216411	0.155696	0.114374	0.200393	0.294718	0.201538	0.043116	0.075388	0.056511	-0.011160	-0.010169	-0.012252	-0.011187	-0.011180	0.206180	0.206180	0.206180	0.207746	0.213569	0.213569	0.213569	0.207746	0.214946	0.214946	0.214946	0.205865	0.214490	0.214490	0.214490	0.205865	0.164308	0.164751	0.164751	0.164751	0.164308	0.150920	0.123135	0.123135	0.123135	0.150920
international_reputation	0.277710	0.045723	0.093912	0.491618	0.380552	0.676484	1.000000	0.110302	0.138444	-0.063367	0.002140	0.017002	0.160887	0.163882	0.123161	0.088488	0.080794	0.005066	0.142636	0.130259	0.124098	0.198796	0.192290	0.121119	0.173613	0.165791	0.197237	0.164953	0.016482	0.014446	0.056832	0.384748	0.004914	0.172561	0.093846	0.068915	0.112206	0.161859	0.145597	0.108258	0.135066	0.228296	0.181447	0.050547	0.073716	0.061860	0.008567	0.008862	0.007609	0.010341	0.007519	0.148768	0.148768	0.148768	0.139487	0.148277	0.148277	0.148277	0.139487	0.149761	0.149761	0.149761	0.138593	0.159101	0.159101	0.159101	0.138593	0.122380	0.134257	0.134257	0.134257	0.122380	0.115759	0.111357	0.111357	0.111357	0.115759
weak_foot	0.077216	-0.164617	-0.129611	0.205962	0.178361	0.140724	0.110302	1.000000	0.326654	-0.043307	-0.037653	0.269767	0.361478	0.332389	0.337112	0.088885	0.215682	-0.233476	0.301782	0.342898	0.192889	0.306543	0.346696	0.333929	0.335910	0.321467	0.273411	0.338561	0.216583	0.205218	0.257929	0.175951	0.204510	0.314252	0.049819	0.204976	-0.020808	0.347918	0.138597	0.056026	0.335953	0.302370	0.316899	0.025367	0.050568	0.030081	-0.229314	-0.225646	-0.227129	-0.227467	-0.231161	0.338631	0.338631	0.338631	0.344032	0.346500	0.346500	0.346500	0.344032	0.345704	0.345704	0.345704	0.335959	0.321143	0.321143	0.321143	0.335959	0.239683	0.223933	0.223933	0.223933	0.239683	0.214493	0.161987	0.161987	0.161987	0.214493
skill_moves	-0.018800	-0.413659	-0.363400	0.243675	0.255135	0.217407	0.138444	0.326654	1.000000	0.005619	0.025072	0.689423	0.748240	0.702009	0.756512	0.184926	0.492610	-0.605735	0.641123	0.704324	0.414778	0.603214	0.693460	0.744145	0.688348	0.643231	0.502428	0.705934	0.559510	0.524851	0.578740	0.194143	0.465349	0.633273	0.006224	0.428807	-0.116498	0.675824	0.240455	0.065951	0.705360	0.514639	0.646602	0.042260	0.097889	0.061889	-0.603506	-0.598528	-0.598083	-0.599330	-0.602399	0.736327	0.736327	0.736327	0.757659	0.753949	0.753949	0.753949	0.757659	0.749065	0.749065	0.749065	0.741494	0.684234	0.684234	0.684234	0.741494	0.531699	0.478510	0.478510	0.478510	0.531699	0.477996	0.352840	0.352840	0.352840	0.477996
team_jersey_number	-0.228214	-0.043021	-0.084685	-0.179027	-0.022922	-0.065684	-0.063367	-0.043307	0.005619	1.000000	0.095653	0.000264	-0.012507	-0.053494	-0.019520	-0.106243	-0.085053	0.009708	-0.041065	0.011293	-0.062259	-0.064427	0.000833	-0.001282	-0.028309	-0.042253	-0.085934	-0.042484	0.026925	0.015438	0.000150	-0.138510	0.033491	-0.032472	-0.080415	-0.088479	-0.122524	-0.014596	-0.124978	-0.130022	-0.008546	-0.074602	-0.005210	-0.103717	-0.102330	-0.090515	-0.000528	-0.006068	-0.005026	-0.006368	-0.001365	-0.031245	-0.031245	-0.031245	-0.026383	-0.028186	-0.028186	-0.028186	-0.026383	-0.033223	-0.033223	-0.033223	-0.034475	-0.058140	-0.058140	-0.058140	-0.034475	-0.078242	-0.092148	-0.092148	-0.092148	-0.078242	-0.083102	-0.098735	-0.098735	-0.098735	-0.083102
contract_valid_until	-0.151552	-0.094536	-0.078132	-0.013182	0.085902	0.092588	0.002140	-0.037653	0.025072	0.095653	1.000000	0.023659	-0.008545	-0.004539	0.010733	-0.003745	-0.012795	-0.002950	-0.006763	0.001818	0.001287	0.010506	-0.009427	0.024451	-0.012891	-0.031438	0.000777	-0.000693	0.045708	0.039327	0.006231	-0.006916	0.025078	-0.013704	0.000822	0.007775	-0.034861	-0.005254	-0.040480	-0.018834	0.003121	-0.017567	-0.004697	0.003763	0.001320	0.009542	-0.010811	-0.011434	-0.012459	-0.013338	-0.010358	-0.000028	-0.000028	-0.000028	0.004863	0.003806	0.003806	0.003806	0.004863	0.003808	0.003808	0.003808	0.005199	0.000444	0.000444	0.000444	0.005199	0.001424	-0.003559	-0.003559	-0.003559	0.001424	0.001553	-0.004776	-0.004776	-0.004776	0.001553
pace	-0.164913	-0.478165	-0.451028	0.144062	0.213147	0.106889	0.017002	0.269767	0.689423	0.000264	0.023659	1.000000	0.800930	0.845809	0.913649	0.528046	0.787867	-0.880618	0.774195	0.697979	0.662325	0.705381	0.683401	0.855954	0.722824	0.646110	0.592139	0.823366	0.796440	0.801431	0.670023	0.104890	0.555840	0.713885	0.159667	0.691661	-0.042270	0.704026	0.471350	0.333314	0.777716	0.435799	0.662849	0.373159	0.414313	0.395538	-0.873950	-0.868274	-0.865116	-0.868614	-0.873302	0.895389	0.895389	0.895389	0.916727	0.902720	0.902720	0.902720	0.916727	0.897215	0.897215	0.897215	0.918541	0.868593	0.868593	0.868593	0.918541	0.832935	0.764888	0.764888	0.764888	0.832935	0.800295	0.684496	0.684496	0.684496	0.800295
shooting	0.048660	-0.378375	-0.320896	0.314795	0.282428	0.212288	0.160887	0.361478	0.748240	-0.012507	-0.008545	0.800930	1.000000	0.874241	0.912135	0.324036	0.705047	-0.763411	0.770076	0.944084	0.647219	0.765730	0.900608	0.887695	0.833123	0.795811	0.640998	0.874899	0.571517	0.560447	0.598910	0.283622	0.456179	0.893741	0.082872	0.590598	0.026450	0.927343	0.419787	0.168501	0.910771	0.632900	0.862874	0.141720	0.201477	0.154064	-0.758078	-0.752263	-0.749882	-0.753777	-0.757140	0.958163	0.958163	0.958163	0.938259	0.951668	0.951668	0.951668	0.938259	0.936173	0.936173	0.936173	0.918355	0.876037	0.876037	0.876037	0.918355	0.698758	0.656536	0.656536	0.656536	0.698758	0.643723	0.527674	0.527674	0.527674	0.643723
passing	0.062909	-0.425457	-0.379390	0.356585	0.314101	0.214805	0.163882	0.332389	0.702009	-0.053494	-0.004539	0.845809	0.874241	1.000000	0.954585	0.628087	0.815567	-0.849273	0.901292	0.745060	0.694386	0.916255	0.770690	0.900838	0.862822	0.830440	0.848701	0.926499	0.559334	0.544179	0.594130	0.309449	0.501967	0.820885	0.103986	0.696999	0.036644	0.831794	0.590796	0.485877	0.835317	0.657614	0.752145	0.471315	0.526870	0.488785	-0.842429	-0.836956	-0.834337	-0.837036	-0.841635	0.940656	0.940656	0.940656	0.965312	0.960350	0.960350	0.960350	0.965312	0.975713	0.975713	0.975713	0.975841	0.987252	0.987252	0.987252	0.975841	0.908033	0.883237	0.883237	0.883237	0.908033	0.869601	0.770362	0.770362	0.770362	0.869601
dribbling	-0.026930	-0.481723	-0.434535	0.300196	0.308350	0.194875	0.123161	0.337112	0.756512	-0.019520	0.010733	0.913649	0.912135	0.954585	1.000000	0.524436	0.792629	-0.867221	0.864360	0.808459	0.683927	0.845044	0.806689	0.965906	0.846774	0.784268	0.735686	0.947548	0.663758	0.643874	0.680627	0.265283	0.564826	0.821653	0.116811	0.684340	-0.024431	0.841241	0.514197	0.356018	0.880288	0.606899	0.776331	0.355289	0.408721	0.375334	-0.860783	-0.855039	-0.852224	-0.855846	-0.859972	0.969502	0.969502	0.969502	0.992113	0.986707	0.986707	0.986707	0.992113	0.988354	0.988354	0.988354	0.990349	0.962021	0.962021	0.962021	0.990349	0.860507	0.812096	0.812096	0.812096	0.860507	0.814175	0.695236	0.695236	0.695236	0.814175
defending	0.100032	-0.094135	-0.094449	0.250692	0.177525	0.086131	0.088488	0.088885	0.184926	-0.106243	-0.003745	0.528046	0.324036	0.628087	0.524436	1.000000	0.793093	-0.675525	0.522255	0.134424	0.693960	0.581427	0.218502	0.415491	0.383564	0.392132	0.604133	0.513999	0.176681	0.201683	0.139224	0.182920	0.145764	0.419787	0.217069	0.598906	0.306040	0.311583	0.759105	0.900588	0.297375	0.182386	0.262622	0.950341	0.962533	0.946758	-0.668346	-0.666287	-0.662787	-0.662892	-0.669583	0.526746	0.526746	0.526746	0.523097	0.516990	0.516990	0.516990	0.523097	0.543815	0.543815	0.543815	0.571736	0.683010	0.683010	0.683010	0.571736	0.875960	0.911355	0.911355	0.911355	0.875960	0.914383	0.966683	0.966683	0.966683	0.914383
physic	0.069155	-0.137255	-0.100458	0.251938	0.182002	0.107833	0.080794	0.215682	0.492610	-0.085053	-0.012795	0.787867	0.705047	0.815567	0.792629	0.793093	1.000000	-0.894652	0.666157	0.546773	0.874765	0.712310	0.574340	0.692573	0.607215	0.587034	0.629375	0.757520	0.387746	0.422150	0.330229	0.193011	0.224638	0.710323	0.237934	0.757023	0.415120	0.616825	0.769158	0.613573	0.645750	0.348580	0.595183	0.644740	0.685592	0.643789	-0.886312	-0.881632	-0.879163	-0.879946	-0.887810	0.848474	0.848474	0.848474	0.808134	0.816910	0.816910	0.816910	0.808134	0.817815	0.817815	0.817815	0.832759	0.874412	0.874412	0.874412	0.832759	0.915032	0.920885	0.920885	0.920885	0.915032	0.915544	0.914284	0.914284	0.914284	0.915544
gk_speed	0.086077	0.337328	0.323379	-0.050719	-0.080463	-0.015566	0.005066	-0.233476	-0.605735	0.009708	-0.002950	-0.880618	-0.763411	-0.849273	-0.867221	-0.675525	-0.894652	1.000000	-0.708400	-0.627433	-0.790570	-0.699086	-0.629532	-0.774641	-0.661731	-0.629654	-0.597333	-0.790668	-0.463939	-0.465746	-0.435507	-0.028167	-0.384146	-0.699581	-0.095970	-0.645944	-0.088828	-0.654188	-0.568473	-0.453756	-0.705665	-0.368787	-0.644618	-0.514756	-0.556996	-0.526258	0.959659	0.951587	0.950879	0.950258	0.958423	-0.882866	-0.882866	-0.882866	-0.870797	-0.870610	-0.870610	-0.870610	-0.870797	-0.871464	-0.871464	-0.871464	-0.883713	-0.887769	-0.887769	-0.887769	-0.883713	-0.881761	-0.857812	-0.857812	-0.857812	-0.881761	-0.866853	-0.813820	-0.813820	-0.813820	-0.866853
attacking_crossing	0.062796	-0.458348	-0.405305	0.337289	0.289771	0.195654	0.142636	0.301782	0.641123	-0.041065	-0.006763	0.774195	0.770076	0.901292	0.864360	0.522255	0.666157	-0.708400	1.000000	0.675164	0.545065	0.800872	0.702745	0.850750	0.831344	0.773463	0.751791	0.834426	0.589846	0.573909	0.610416	0.296189	0.520314	0.721327	0.087195	0.633683	-0.043699	0.759907	0.486414	0.413056	0.781636	0.590592	0.663582	0.405365	0.448174	0.427599	-0.706749	-0.702701	-0.699935	-0.703763	-0.705759	0.822018	0.822018	0.822018	0.875131	0.851572	0.851572	0.851572	0.875131	0.862944	0.862944	0.862944	0.881982	0.859988	0.859988	0.859988	0.881982	0.808197	0.751275	0.751275	0.751275	0.808197	0.764308	0.640622	0.640622	0.640622	0.764308
attacking_finishing	0.005349	-0.358111	-0.312001	0.258967	0.255918	0.194837	0.130259	0.342898	0.704324	0.011293	0.001818	0.697979	0.944084	0.745060	0.808459	0.134424	0.546773	-0.627433	0.675164	1.000000	0.526821	0.653716	0.863921	0.819004	0.754751	0.710470	0.517743	0.779758	0.544159	0.528919	0.570481	0.248193	0.429667	0.783201	0.043537	0.477330	-0.034551	0.854079	0.267060	0.000603	0.870155	0.595582	0.813355	-0.029025	0.026011	-0.020106	-0.627670	-0.622927	-0.619716	-0.626061	-0.626086	0.859337	0.859337	0.859337	0.838406	0.852345	0.852345	0.852345	0.838406	0.830024	0.830024	0.830024	0.807571	0.739998	0.739998	0.739998	0.807571	0.528117	0.477998	0.477998	0.477998	0.528117	0.466539	0.341281	0.341281	0.341281	0.466539
attacking_heading_accuracy	0.080368	-0.028599	-0.033478	0.285335	0.225003	0.135334	0.124098	0.192889	0.414778	-0.062259	0.001287	0.662325	0.647219	0.694386	0.683927	0.693960	0.874765	-0.790570	0.545065	0.526821	1.000000	0.623552	0.544204	0.598998	0.514431	0.491310	0.523356	0.675608	0.276705	0.320428	0.221345	0.218165	0.116292	0.657084	0.254868	0.592849	0.382820	0.550307	0.659538	0.512355	0.579896	0.269583	0.571513	0.553592	0.582708	0.543606	-0.787212	-0.784757	-0.779547	-0.782220	-0.787717	0.775686	0.775686	0.775686	0.702986	0.723562	0.723562	0.723562	0.702986	0.714178	0.714178	0.714178	0.719612	0.758542	0.758542	0.758542	0.719612	0.781133	0.794573	0.794573	0.794573	0.781133	0.789995	0.811381	0.811381	0.811381	0.789995
attacking_short_passing	0.086728	-0.341943	-0.302296	0.462311	0.419149	0.272554	0.198796	0.306543	0.603214	-0.064427	0.010506	0.705381	0.765730	0.916255	0.845044	0.581427	0.712310	-0.699086	0.800872	0.653716	0.623552	1.000000	0.681397	0.811591	0.767307	0.737667	0.884123	0.873523	0.473710	0.463438	0.521574	0.409666	0.429366	0.753900	0.116423	0.634016	0.082916	0.752893	0.571531	0.487002	0.735982	0.640776	0.663619	0.450958	0.505908	0.462825	-0.694399	-0.689938	-0.686612	-0.689461	-0.693534	0.829176	0.829176	0.829176	0.853626	0.852871	0.852871	0.852871	0.853626	0.875312	0.875312	0.875312	0.867623	0.900025	0.900025	0.900025	0.867623	0.817747	0.813068	0.813068	0.813068	0.817747	0.783080	0.700933	0.700933	0.700933	0.783080
attacking_volleys	0.061369	-0.344828	-0.291449	0.313773	0.279155	0.229246	0.192290	0.346696	0.693460	0.000833	-0.009427	0.683401	0.900608	0.770690	0.806689	0.218502	0.574340	-0.629532	0.702745	0.863921	0.544204	0.681397	1.000000	0.805800	0.793098	0.745444	0.566144	0.782048	0.513073	0.495831	0.559215	0.293150	0.427023	0.797948	0.066621	0.499465	-0.007675	0.846999	0.339563	0.098818	0.836505	0.602356	0.795701	0.062621	0.118518	0.076556	-0.629848	-0.625313	-0.621765	-0.627386	-0.628249	0.843690	0.843690	0.843690	0.829032	0.840686	0.840686	0.840686	0.829032	0.826355	0.826355	0.826355	0.807050	0.760627	0.760627	0.760627	0.807050	0.577657	0.535637	0.535637	0.535637	0.577657	0.521969	0.407616	0.407616	0.407616	0.521969
skill_dribbling	-0.051007	-0.475107	-0.431251	0.301068	0.328817	0.204294	0.121119	0.333929	0.744145	-0.001282	0.024451	0.855954	0.887695	0.900838	0.965906	0.415491	0.692573	-0.774641	0.850750	0.819004	0.598998	0.811591	0.805800	1.000000	0.838454	0.768556	0.701822	0.918433	0.668251	0.645656	0.673653	0.270693	0.555582	0.794862	0.078759	0.631916	-0.063770	0.838788	0.431736	0.270540	0.878515	0.623687	0.760631	0.262768	0.315544	0.284408	-0.772031	-0.766865	-0.763112	-0.769358	-0.771108	0.919107	0.919107	0.919107	0.950743	0.943775	0.943775	0.943775	0.950743	0.942851	0.942851	0.942851	0.943085	0.898746	0.898746	0.898746	0.943085	0.772177	0.715617	0.715617	0.715617	0.772177	0.718024	0.586209	0.586209	0.586209	0.718024
skill_curve	0.070079	-0.421572	-0.368087	0.354916	0.316465	0.226008	0.173613	0.335910	0.688348	-0.028309	-0.012891	0.722824	0.833123	0.862822	0.846774	0.383564	0.607215	-0.661731	0.831344	0.754751	0.514431	0.767307	0.793098	0.838454	1.000000	0.850194	0.699538	0.825819	0.550957	0.529230	0.606768	0.316768	0.495136	0.770820	0.057323	0.564913	-0.052682	0.826953	0.411241	0.282946	0.807997	0.635218	0.746328	0.246658	0.301985	0.268331	-0.661427	-0.656669	-0.654681	-0.660964	-0.660563	0.829595	0.829595	0.829595	0.857413	0.852536	0.852536	0.852536	0.857413	0.856633	0.856633	0.856633	0.850916	0.827431	0.827431	0.827431	0.850916	0.702371	0.659306	0.659306	0.659306	0.702371	0.651881	0.530464	0.530464	0.530464	0.651881
skill_fk_accuracy	0.122131	-0.384103	-0.326886	0.329594	0.261308	0.204509	0.165791	0.321467	0.643231	-0.042253	-0.031438	0.646110	0.795811	0.830440	0.784268	0.392132	0.587034	-0.629654	0.773463	0.710470	0.491310	0.737667	0.745444	0.768556	0.850194	1.000000	0.693794	0.770411	0.455804	0.427267	0.531066	0.299935	0.445329	0.749776	0.037720	0.526272	-0.028095	0.804177	0.418455	0.305571	0.744764	0.618790	0.738011	0.262319	0.318619	0.280936	-0.628741	-0.623520	-0.620866	-0.627084	-0.626592	0.780259	0.780259	0.780259	0.800120	0.799696	0.799696	0.799696	0.800120	0.807038	0.807038	0.807038	0.795987	0.791672	0.791672	0.791672	0.795987	0.672190	0.645073	0.645073	0.645073	0.672190	0.627274	0.524233	0.524233	0.524233	0.627274
skill_long_passing	0.139071	-0.319234	-0.279506	0.449014	0.374957	0.253297	0.197237	0.273411	0.502428	-0.085934	0.000777	0.592139	0.640998	0.848701	0.735686	0.604133	0.629375	-0.597333	0.751791	0.517743	0.523356	0.884123	0.566144	0.701822	0.699538	0.693794	1.000000	0.766071	0.385430	0.372229	0.456365	0.388877	0.387601	0.659658	0.109985	0.583382	0.067763	0.667167	0.556592	0.547917	0.607318	0.611578	0.550824	0.504891	0.550589	0.517332	-0.593282	-0.588155	-0.585491	-0.588310	-0.591246	0.704655	0.704655	0.704655	0.741146	0.735400	0.735400	0.735400	0.741146	0.768470	0.768470	0.768470	0.765193	0.823421	0.823421	0.823421	0.765193	0.769808	0.781576	0.781576	0.781576	0.769808	0.744346	0.677501	0.677501	0.677501	0.744346
skill_ball_control	0.029144	-0.394236	-0.353959	0.406070	0.391883	0.245157	0.164953	0.338561	0.705934	-0.042484	-0.000693	0.823366	0.874899	0.926499	0.947548	0.513999	0.757520	-0.790668	0.834426	0.779758	0.675608	0.873523	0.782048	0.918433	0.825819	0.770411	0.766071	1.000000	0.585556	0.573960	0.609808	0.355175	0.486774	0.822409	0.118261	0.655850	0.042078	0.827870	0.530494	0.376468	0.843712	0.628627	0.758144	0.357393	0.410996	0.371727	-0.784465	-0.780271	-0.776465	-0.779936	-0.784129	0.926808	0.926808	0.926808	0.943087	0.943181	0.943181	0.943181	0.943087	0.947908	0.947908	0.947908	0.943578	0.933271	0.933271	0.933271	0.943578	0.824830	0.792925	0.792925	0.792925	0.824830	0.780885	0.675669	0.675669	0.675669	0.780885
movement_acceleration	-0.224824	-0.515279	-0.484357	0.157273	0.265961	0.152016	0.016482	0.216583	0.559510	0.026925	0.045708	0.796440	0.571517	0.559334	0.663758	0.176681	0.387746	-0.463939	0.589846	0.544159	0.276705	0.473710	0.513073	0.668251	0.550957	0.455804	0.385430	0.585556	1.000000	0.865255	0.734950	0.130906	0.603083	0.478476	0.168976	0.508321	-0.222655	0.525611	0.183487	0.075532	0.601232	0.366858	0.459133	0.080902	0.106630	0.110050	-0.493847	-0.491974	-0.488022	-0.493843	-0.493340	0.604166	0.604166	0.604166	0.661794	0.635606	0.635606	0.635606	0.661794	0.626725	0.626725	0.626725	0.649924	0.554812	0.554812	0.554812	0.649924	0.493498	0.398022	0.398022	0.398022	0.493498	0.450753	0.298822	0.298822	0.298822	0.450753
movement_sprint_speed	-0.218302	-0.435221	-0.413137	0.176556	0.278954	0.153989	0.014446	0.205218	0.524851	0.015438	0.039327	0.801431	0.560447	0.544179	0.643874	0.201683	0.422150	-0.465746	0.573909	0.528919	0.320428	0.463438	0.495831	0.645656	0.529230	0.427267	0.372229	0.573960	0.865255	1.000000	0.681146	0.135018	0.528762	0.486232	0.186220	0.519681	-0.139247	0.505146	0.216984	0.094755	0.585666	0.333093	0.448614	0.105869	0.131338	0.132122	-0.495886	-0.495793	-0.490757	-0.497843	-0.496280	0.603702	0.603702	0.603702	0.647820	0.624833	0.624833	0.624833	0.647820	0.612193	0.612193	0.612193	0.638277	0.547184	0.547184	0.547184	0.638277	0.501434	0.408964	0.408964	0.408964	0.501434	0.463781	0.325201	0.325201	0.325201	0.463781
movement_agility	-0.073392	-0.599136	-0.546209	0.211860	0.246533	0.159247	0.056832	0.257929	0.578740	0.000150	0.006231	0.670023	0.598910	0.594130	0.680627	0.139224	0.330229	-0.435507	0.610416	0.570481	0.221345	0.521574	0.559215	0.673653	0.606768	0.531066	0.456365	0.609808	0.734950	0.681146	1.000000	0.208564	0.682670	0.491919	0.182293	0.459095	-0.295513	0.576066	0.179222	0.065143	0.618571	0.485716	0.494055	0.038587	0.067829	0.067121	-0.442482	-0.440033	-0.439042	-0.442835	-0.441984	0.594003	0.594003	0.594003	0.661655	0.636283	0.636283	0.636283	0.661655	0.640666	0.640666	0.640666	0.643769	0.570597	0.570597	0.570597	0.643769	0.463525	0.385108	0.385108	0.385108	0.463525	0.411821	0.259553	0.259553	0.259553	0.411821
movement_reactions	0.395858	0.003806	0.071590	0.792814	0.590323	0.471799	0.384748	0.175951	0.194143	-0.138510	-0.006916	0.104890	0.283622	0.309449	0.265283	0.182920	0.193011	-0.028167	0.296189	0.248193	0.218165	0.409666	0.293150	0.270693	0.316768	0.299935	0.388877	0.355175	0.130906	0.135018	0.208564	1.000000	0.074565	0.320461	0.203560	0.272519	0.214872	0.317900	0.336892	0.249985	0.297775	0.429489	0.257361	0.139241	0.160398	0.133325	-0.000527	0.002699	-0.000727	0.005807	-0.001361	0.284001	0.284001	0.284001	0.281018	0.292871	0.292871	0.292871	0.281018	0.295602	0.295602	0.295602	0.282749	0.313722	0.313722	0.313722	0.282749	0.260861	0.273106	0.273106	0.273106	0.260861	0.245538	0.222267	0.222267	0.222267	0.245538
movement_balance	-0.123581	-0.787934	-0.697291	0.037695	0.109251	0.065007	0.004914	0.204510	0.465349	0.033491	0.025078	0.555840	0.456179	0.501967	0.564826	0.145764	0.224638	-0.384146	0.520314	0.429667	0.116292	0.429366	0.427023	0.555582	0.495136	0.445329	0.387601	0.486774	0.603083	0.528762	0.682670	0.074565	1.000000	0.363177	0.143657	0.353096	-0.446657	0.451306	0.115319	0.083790	0.487201	0.376408	0.386284	0.079182	0.097653	0.111921	-0.401418	-0.399526	-0.395503	-0.401498	-0.399564	0.459218	0.459218	0.459218	0.537243	0.510825	0.510825	0.510825	0.537243	0.521549	0.521549	0.521549	0.528261	0.473680	0.473680	0.473680	0.528261	0.400874	0.332768	0.332768	0.332768	0.400874	0.358966	0.222051	0.222051	0.222051	0.358966
power_shot_power	0.088910	-0.281572	-0.220455	0.376679	0.322904	0.217239	0.172561	0.314252	0.633273	-0.032472	-0.013704	0.713885	0.893741	0.820885	0.821653	0.419787	0.710323	-0.699581	0.721327	0.783201	0.657084	0.753900	0.797948	0.794862	0.770820	0.749776	0.659658	0.822409	0.478476	0.486232	0.491919	0.320461	0.363177	1.000000	0.115005	0.589868	0.135769	0.862322	0.508822	0.292827	0.792400	0.567777	0.772706	0.266954	0.320677	0.276584	-0.697839	-0.693702	-0.690823	-0.694882	-0.696986	0.876177	0.876177	0.876177	0.844244	0.862492	0.862492	0.862492	0.844244	0.850102	0.850102	0.850102	0.836326	0.828000	0.828000	0.828000	0.836326	0.705215	0.684018	0.684018	0.684018	0.705215	0.665902	0.585250	0.585250	0.585250	0.665902
power_jumping	0.121634	-0.073728	-0.024147	0.237238	0.160963	0.110937	0.093846	0.049819	0.006224	-0.080415	0.000822	0.159667	0.082872	0.103986	0.116811	0.217069	0.237934	-0.095970	0.087195	0.043537	0.254868	0.116423	0.066621	0.078759	0.057323	0.037720	0.109985	0.118261	0.168976	0.186220	0.182293	0.203560	0.143657	0.115005	1.000000	0.242498	0.172379	0.069583	0.264087	0.210933	0.080256	0.034678	0.070303	0.189044	0.185032	0.192103	-0.101529	-0.101283	-0.099919	-0.099698	-0.102472	0.139095	0.139095	0.139095	0.118867	0.120011	0.120011	0.120011	0.118867	0.115986	0.115986	0.115986	0.126938	0.140109	0.140109	0.140109	0.126938	0.196337	0.196919	0.196919	0.196919	0.196337	0.209495	0.236918	0.236918	0.236918	0.209495
power_stamina	0.023215	-0.259871	-0.231913	0.303921	0.246595	0.164818	0.068915	0.204976	0.428807	-0.088479	0.007775	0.691661	0.590598	0.696999	0.684340	0.598906	0.757023	-0.645944	0.633683	0.477330	0.592849	0.634016	0.499465	0.631916	0.564913	0.526272	0.583382	0.655850	0.508321	0.519681	0.459095	0.272519	0.353096	0.589868	0.242498	1.000000	0.191492	0.564325	0.583810	0.501858	0.591054	0.397458	0.484088	0.496106	0.529711	0.503195	-0.642126	-0.639228	-0.636662	-0.638767	-0.643495	0.688676	0.688676	0.688676	0.695161	0.689726	0.689726	0.689726	0.695161	0.695555	0.695555	0.695555	0.720161	0.737206	0.737206	0.737206	0.720161	0.764014	0.736461	0.736461	0.736461	0.764014	0.748729	0.685520	0.685520	0.685520	0.748729
power_strength	0.259827	0.531675	0.595008	0.292941	0.128617	0.101879	0.112206	-0.020808	-0.116498	-0.122524	-0.034861	-0.042270	0.026450	0.036644	-0.024431	0.306040	0.415120	-0.088828	-0.043699	-0.034551	0.382820	0.082916	-0.007675	-0.063770	-0.052682	-0.028095	0.067763	0.042078	-0.222655	-0.139247	-0.295513	0.214872	-0.446657	0.135769	0.172379	0.191492	1.000000	0.003175	0.391389	0.301374	-0.020422	-0.053100	0.021208	0.286243	0.295564	0.255583	-0.084551	-0.081549	-0.084190	-0.078650	-0.087483	0.098058	0.098058	0.098058	0.004797	0.032616	0.032616	0.032616	0.004797	0.023525	0.023525	0.023525	0.024305	0.093435	0.093435	0.093435	0.024305	0.168889	0.233944	0.233944	0.233944	0.168889	0.201371	0.323689	0.323689	0.323689	0.201371
power_long_shots	0.076456	-0.379441	-0.316240	0.341751	0.296538	0.216411	0.161859	0.347918	0.675824	-0.014596	-0.005254	0.704026	0.927343	0.831794	0.841241	0.311583	0.616825	-0.654188	0.759907	0.854079	0.550307	0.752893	0.846999	0.838788	0.826953	0.804177	0.667167	0.827870	0.525611	0.505146	0.576066	0.317900	0.451306	0.862322	0.069583	0.564325	0.003175	1.000000	0.400693	0.203746	0.845436	0.647124	0.789812	0.159072	0.216314	0.171608	-0.654598	-0.650759	-0.646422	-0.651769	-0.653411	0.867660	0.867660	0.867660	0.864848	0.874767	0.874767	0.874767	0.864848	0.868562	0.868562	0.868562	0.846400	0.822542	0.822542	0.822542	0.846400	0.652589	0.617293	0.617293	0.617293	0.652589	0.598755	0.484314	0.484314	0.484314	0.598755
mentality_aggression	0.237996	-0.045248	-0.002919	0.391474	0.241544	0.155696	0.145597	0.138597	0.240455	-0.124978	-0.040480	0.471350	0.419787	0.590796	0.514197	0.759105	0.769158	-0.568473	0.486414	0.267060	0.659538	0.571531	0.339563	0.431736	0.411241	0.418455	0.556592	0.530494	0.183487	0.216984	0.179222	0.336892	0.115319	0.508822	0.264087	0.583810	0.391389	0.400693	1.000000	0.695470	0.390661	0.276910	0.344994	0.680253	0.709337	0.678957	-0.558962	-0.555333	-0.553184	-0.551825	-0.560739	0.554021	0.554021	0.554021	0.526201	0.533836	0.533836	0.533836	0.526201	0.545512	0.545512	0.545512	0.558113	0.639390	0.639390	0.639390	0.558113	0.734497	0.775185	0.775185	0.775185	0.734497	0.752965	0.800709	0.800709	0.800709	0.752965
mentality_interceptions	0.167387	-0.038329	-0.035367	0.306587	0.199210	0.114374	0.108258	0.056026	0.065951	-0.130022	-0.018834	0.333314	0.168501	0.485877	0.356018	0.900588	0.613573	-0.453756	0.413056	0.000603	0.512355	0.487002	0.098818	0.270540	0.282946	0.305571	0.547917	0.376468	0.075532	0.094755	0.065143	0.249985	0.083790	0.292827	0.210933	0.501858	0.301374	0.203746	0.695470	1.000000	0.168677	0.160827	0.127833	0.876480	0.890864	0.873848	-0.453764	-0.453750	-0.448732	-0.449937	-0.455025	0.346869	0.346869	0.346869	0.354886	0.347690	0.347690	0.347690	0.354886	0.380442	0.380442	0.380442	0.404170	0.530829	0.530829	0.530829	0.404170	0.728475	0.776615	0.776615	0.776615	0.728475	0.767959	0.829185	0.829185	0.829185	0.767959
mentality_positioning	0.014522	-0.414091	-0.360297	0.286826	0.272169	0.200393	0.135066	0.335953	0.705360	-0.008546	0.003121	0.777716	0.910771	0.835317	0.880288	0.297375	0.645750	-0.705665	0.781636	0.870155	0.579896	0.735982	0.836505	0.878515	0.807997	0.744764	0.607318	0.843712	0.601232	0.585666	0.618571	0.297775	0.487201	0.792400	0.080256	0.591054	-0.020422	0.845436	0.390661	0.168677	1.000000	0.631142	0.786006	0.134477	0.192662	0.153813	-0.706378	-0.700799	-0.698006	-0.703979	-0.705836	0.903281	0.903281	0.903281	0.904368	0.911966	0.911966	0.911966	0.904368	0.898125	0.898125	0.898125	0.887381	0.835864	0.835864	0.835864	0.887381	0.668715	0.616742	0.616742	0.616742	0.668715	0.612659	0.486627	0.486627	0.486627	0.612659
mentality_vision	0.160532	-0.307677	-0.253481	0.454645	0.380165	0.294718	0.228296	0.302370	0.514639	-0.074602	-0.017567	0.435799	0.632900	0.657614	0.606899	0.182386	0.348580	-0.368787	0.590592	0.595582	0.269583	0.640776	0.602356	0.623687	0.635218	0.618790	0.611578	0.628627	0.366858	0.333093	0.485716	0.429489	0.376408	0.567777	0.034678	0.397458	-0.053100	0.647124	0.276910	0.160827	0.631142	1.000000	0.553786	0.074908	0.128337	0.090808	-0.339098	-0.331159	-0.329005	-0.328355	-0.336131	0.588812	0.588812	0.588812	0.624646	0.628434	0.628434	0.628434	0.624646	0.643983	0.643983	0.643983	0.616911	0.614825	0.614825	0.614825	0.616911	0.446975	0.431218	0.431218	0.431218	0.446975	0.397495	0.293680	0.293680	0.293680	0.397495
mentality_penalties	0.071897	-0.320547	-0.265101	0.288142	0.251420	0.201538	0.181447	0.316899	0.646602	-0.005210	-0.004697	0.662849	0.862874	0.752145	0.776331	0.262622	0.595183	-0.644618	0.663582	0.813355	0.571513	0.663619	0.795701	0.760631	0.746328	0.738011	0.550824	0.758144	0.459133	0.448614	0.494055	0.257361	0.386284	0.772706	0.070303	0.484088	0.021208	0.789812	0.344994	0.127833	0.786006	0.553786	1.000000	0.104479	0.156818	0.114689	-0.644510	-0.640010	-0.637325	-0.642691	-0.645470	0.819145	0.819145	0.819145	0.799166	0.812367	0.812367	0.812367	0.799166	0.799480	0.799480	0.799480	0.781754	0.746818	0.746818	0.746818	0.781754	0.585662	0.552169	0.552169	0.552169	0.585662	0.537874	0.440939	0.440939	0.440939	0.537874
defending_marking	0.095487	-0.044097	-0.051303	0.199397	0.129611	0.043116	0.050547	0.025367	0.042260	-0.103717	0.003763	0.373159	0.141720	0.471315	0.355289	0.950341	0.644740	-0.514756	0.405365	-0.029025	0.553592	0.450958	0.062621	0.262768	0.246658	0.262319	0.504891	0.357393	0.080902	0.105869	0.038587	0.139241	0.079182	0.266954	0.189044	0.496106	0.286243	0.159072	0.680253	0.876480	0.134477	0.074908	0.104479	1.000000	0.946202	0.947263	-0.512059	-0.511903	-0.508248	-0.509167	-0.513108	0.343647	0.343647	0.343647	0.349419	0.338706	0.338706	0.338706	0.349419	0.369532	0.369532	0.369532	0.401499	0.519940	0.519940	0.519940	0.401499	0.749758	0.791030	0.791030	0.791030	0.749758	0.797458	0.868021	0.868021	0.868021	0.797458
defending_standing_tackle	0.085014	-0.052524	-0.063893	0.224607	0.162444	0.075388	0.073716	0.050568	0.097889	-0.102330	0.001320	0.414313	0.201477	0.526870	0.408721	0.962533	0.685592	-0.556996	0.448174	0.026011	0.582708	0.505908	0.118518	0.315544	0.301985	0.318619	0.550589	0.410996	0.106630	0.131338	0.067829	0.160398	0.097653	0.320677	0.185032	0.529711	0.295564	0.216314	0.709337	0.890864	0.192662	0.128337	0.156818	0.946202	1.000000	0.959425	-0.553528	-0.552902	-0.549392	-0.550899	-0.555346	0.399781	0.399781	0.399781	0.405141	0.396203	0.396203	0.396203	0.405141	0.427256	0.427256	0.427256	0.456293	0.576335	0.576335	0.576335	0.456293	0.788744	0.830142	0.830142	0.830142	0.788744	0.833216	0.896043	0.896043	0.896043	0.833216
defending_sliding_tackle	0.068744	-0.071170	-0.081469	0.191321	0.139049	0.056511	0.061860	0.030081	0.061889	-0.090515	0.009542	0.395538	0.154064	0.488785	0.375334	0.946758	0.643789	-0.526258	0.427599	-0.020106	0.543606	0.462825	0.076556	0.284408	0.268331	0.280936	0.517332	0.371727	0.110050	0.132122	0.067121	0.133325	0.111921	0.276584	0.192103	0.503195	0.255583	0.171608	0.678957	0.873848	0.153813	0.090808	0.114689	0.947263	0.959425	1.000000	-0.524035	-0.522774	-0.519861	-0.520562	-0.525231	0.356257	0.356257	0.356257	0.368155	0.355418	0.355418	0.355418	0.368155	0.386849	0.386849	0.386849	0.419834	0.534961	0.534961	0.534961	0.419834	0.764351	0.798299	0.798299	0.798299	0.764351	0.811201	0.870404	0.870404	0.870404	0.811201
goalkeeping_diving	0.110753	0.345080	0.337671	-0.013305	-0.054610	-0.011160	0.008567	-0.229314	-0.603506	-0.000528	-0.010811	-0.873950	-0.758078	-0.842429	-0.860783	-0.668346	-0.886312	0.959659	-0.706749	-0.627670	-0.787212	-0.694399	-0.629848	-0.772031	-0.661427	-0.628741	-0.593282	-0.784465	-0.493847	-0.495886	-0.442482	-0.000527	-0.401418	-0.697839	-0.101529	-0.642126	-0.084551	-0.654598	-0.558962	-0.453764	-0.706378	-0.339098	-0.644510	-0.512059	-0.553528	-0.524035	1.000000	0.965109	0.961118	0.965527	0.972006	-0.876228	-0.876228	-0.876228	-0.864253	-0.864122	-0.864122	-0.864122	-0.864253	-0.864870	-0.864870	-0.864870	-0.876887	-0.880697	-0.880697	-0.880697	-0.876887	-0.873948	-0.849979	-0.849979	-0.849979	-0.873948	-0.859059	-0.806065	-0.806065	-0.806065	-0.859059
goalkeeping_handling	0.118913	0.344673	0.339249	-0.008285	-0.052694	-0.010169	0.008862	-0.225646	-0.598528	-0.006068	-0.011434	-0.868274	-0.752263	-0.836956	-0.855039	-0.666287	-0.881632	0.951587	-0.702701	-0.622927	-0.784757	-0.689938	-0.625313	-0.766865	-0.656669	-0.623520	-0.588155	-0.780271	-0.491974	-0.495793	-0.440033	0.002699	-0.399526	-0.693702	-0.101283	-0.639228	-0.081549	-0.650759	-0.555333	-0.453750	-0.700799	-0.331159	-0.640010	-0.511903	-0.552902	-0.522774	0.965109	1.000000	0.960160	0.965378	0.966128	-0.870298	-0.870298	-0.870298	-0.858322	-0.858170	-0.858170	-0.858170	-0.858322	-0.858960	-0.858960	-0.858960	-0.871058	-0.875173	-0.875173	-0.875173	-0.871058	-0.869498	-0.845903	-0.845903	-0.845903	-0.869498	-0.854817	-0.802532	-0.802532	-0.802532	-0.854817
goalkeeping_kicking	0.114074	0.340714	0.334518	-0.011916	-0.054882	-0.012252	0.007609	-0.227129	-0.598083	-0.005026	-0.012459	-0.865116	-0.749882	-0.834337	-0.852224	-0.662787	-0.879163	0.950879	-0.699935	-0.619716	-0.779547	-0.686612	-0.621765	-0.763112	-0.654681	-0.620866	-0.585491	-0.776465	-0.488022	-0.490757	-0.439042	-0.000727	-0.395503	-0.690823	-0.099919	-0.636662	-0.084190	-0.646422	-0.553184	-0.448732	-0.698006	-0.329005	-0.637325	-0.508248	-0.549392	-0.519861	0.961118	0.960160	1.000000	0.959859	0.962098	-0.867379	-0.867379	-0.867379	-0.855536	-0.855347	-0.855347	-0.855347	-0.855536	-0.856093	-0.856093	-0.856093	-0.868190	-0.871984	-0.871984	-0.871984	-0.868190	-0.865872	-0.842329	-0.842329	-0.842329	-0.865872	-0.851173	-0.799097	-0.799097	-0.799097	-0.851173
goalkeeping_positioning	0.126850	0.346370	0.340144	-0.004463	-0.053324	-0.011187	0.010341	-0.227467	-0.599330	-0.006368	-0.013338	-0.868614	-0.753777	-0.837036	-0.855846	-0.662892	-0.879946	0.950258	-0.703763	-0.626061	-0.782220	-0.689461	-0.627386	-0.769358	-0.660964	-0.627084	-0.588310	-0.779936	-0.493843	-0.497843	-0.442835	0.005807	-0.401498	-0.694882	-0.099698	-0.638767	-0.078650	-0.651769	-0.551825	-0.449937	-0.703979	-0.328355	-0.642691	-0.509167	-0.550899	-0.520562	0.965527	0.965378	0.959859	1.000000	0.966918	-0.870844	-0.870844	-0.870844	-0.859059	-0.858909	-0.858909	-0.858909	-0.859059	-0.859540	-0.859540	-0.859540	-0.871543	-0.874876	-0.874876	-0.874876	-0.871543	-0.867852	-0.843831	-0.843831	-0.843831	-0.867852	-0.852858	-0.799867	-0.799867	-0.799867	-0.852858
goalkeeping_reflexes	0.109512	0.343593	0.334678	-0.013170	-0.053684	-0.011180	0.007519	-0.231161	-0.602399	-0.001365	-0.010358	-0.873302	-0.757140	-0.841635	-0.859972	-0.669583	-0.887810	0.958423	-0.705759	-0.626086	-0.787717	-0.693534	-0.628249	-0.771108	-0.660563	-0.626592	-0.591246	-0.784129	-0.493340	-0.496280	-0.441984	-0.001361	-0.399564	-0.696986	-0.102472	-0.643495	-0.087483	-0.653411	-0.560739	-0.455025	-0.705836	-0.336131	-0.645470	-0.513108	-0.555346	-0.525231	0.972006	0.966128	0.962098	0.966918	1.000000	-0.875834	-0.875834	-0.875834	-0.863498	-0.863480	-0.863480	-0.863480	-0.863498	-0.864134	-0.864134	-0.864134	-0.876241	-0.880201	-0.880201	-0.880201	-0.876241	-0.874259	-0.850509	-0.850509	-0.850509	-0.874259	-0.859623	-0.807174	-0.807174	-0.807174	-0.859623
ls	0.022931	-0.371797	-0.325837	0.321237	0.297491	0.206180	0.148768	0.338631	0.736327	-0.031245	-0.000028	0.895389	0.958163	0.940656	0.969502	0.526746	0.848474	-0.882866	0.822018	0.859337	0.775686	0.829176	0.843690	0.919107	0.829595	0.780259	0.704655	0.926808	0.604166	0.603702	0.594003	0.284001	0.459218	0.876177	0.139095	0.688676	0.098058	0.867660	0.554021	0.346869	0.903281	0.588812	0.819145	0.343647	0.399781	0.356257	-0.876228	-0.870298	-0.867379	-0.870844	-0.875834	1.000000	1.000000	1.000000	0.985759	0.993289	0.993289	0.993289	0.985759	0.985274	0.985274	0.985274	0.981555	0.959796	0.959796	0.959796	0.981555	0.850656	0.814169	0.814169	0.814169	0.850656	0.809747	0.713309	0.713309	0.713309	0.809747
st	0.022931	-0.371797	-0.325837	0.321237	0.297491	0.206180	0.148768	0.338631	0.736327	-0.031245	-0.000028	0.895389	0.958163	0.940656	0.969502	0.526746	0.848474	-0.882866	0.822018	0.859337	0.775686	0.829176	0.843690	0.919107	0.829595	0.780259	0.704655	0.926808	0.604166	0.603702	0.594003	0.284001	0.459218	0.876177	0.139095	0.688676	0.098058	0.867660	0.554021	0.346869	0.903281	0.588812	0.819145	0.343647	0.399781	0.356257	-0.876228	-0.870298	-0.867379	-0.870844	-0.875834	1.000000	1.000000	1.000000	0.985759	0.993289	0.993289	0.993289	0.985759	0.985274	0.985274	0.985274	0.981555	0.959796	0.959796	0.959796	0.981555	0.850656	0.814169	0.814169	0.814169	0.850656	0.809747	0.713309	0.713309	0.713309	0.809747
rs	0.022931	-0.371797	-0.325837	0.321237	0.297491	0.206180	0.148768	0.338631	0.736327	-0.031245	-0.000028	0.895389	0.958163	0.940656	0.969502	0.526746	0.848474	-0.882866	0.822018	0.859337	0.775686	0.829176	0.843690	0.919107	0.829595	0.780259	0.704655	0.926808	0.604166	0.603702	0.594003	0.284001	0.459218	0.876177	0.139095	0.688676	0.098058	0.867660	0.554021	0.346869	0.903281	0.588812	0.819145	0.343647	0.399781	0.356257	-0.876228	-0.870298	-0.867379	-0.870844	-0.875834	1.000000	1.000000	1.000000	0.985759	0.993289	0.993289	0.993289	0.985759	0.985274	0.985274	0.985274	0.981555	0.959796	0.959796	0.959796	0.981555	0.850656	0.814169	0.814169	0.814169	0.850656	0.809747	0.713309	0.713309	0.713309	0.809747
lw	-0.003591	-0.455301	-0.407746	0.314071	0.306260	0.207746	0.139487	0.344032	0.757659	-0.026383	0.004863	0.916727	0.938259	0.965312	0.992113	0.523097	0.808134	-0.870797	0.875131	0.838406	0.702986	0.853626	0.829032	0.950743	0.857413	0.800120	0.741146	0.943087	0.661794	0.647820	0.661655	0.281018	0.537243	0.844244	0.118867	0.695161	0.004797	0.864848	0.526201	0.354886	0.904368	0.624646	0.799166	0.349419	0.405141	0.368155	-0.864253	-0.858322	-0.855536	-0.859059	-0.863498	0.985759	0.985759	0.985759	1.000000	0.997293	0.997293	0.997293	1.000000	0.996731	0.996731	0.996731	0.997319	0.970024	0.970024	0.970024	0.997319	0.863179	0.816256	0.816256	0.816256	0.863179	0.817004	0.699402	0.699402	0.699402	0.817004
lf	0.009109	-0.426990	-0.379890	0.324193	0.310361	0.213569	0.148277	0.346500	0.753949	-0.028186	0.003806	0.902720	0.951668	0.960350	0.986707	0.516990	0.816910	-0.870610	0.851572	0.852345	0.723562	0.852871	0.840686	0.943775	0.852536	0.799696	0.735400	0.943181	0.635606	0.624833	0.636283	0.292871	0.510825	0.862492	0.120011	0.689726	0.032616	0.874767	0.533836	0.347690	0.911966	0.628434	0.812367	0.338706	0.396203	0.355418	-0.864122	-0.858170	-0.855347	-0.858909	-0.863480	0.993289	0.993289	0.993289	0.997293	1.000000	1.000000	1.000000	0.997293	0.997128	0.997128	0.997128	0.993663	0.970026	0.970026	0.970026	0.993663	0.854643	0.813408	0.813408	0.813408	0.854643	0.809238	0.698344	0.698344	0.698344	0.809238
cf	0.009109	-0.426990	-0.379890	0.324193	0.310361	0.213569	0.148277	0.346500	0.753949	-0.028186	0.003806	0.902720	0.951668	0.960350	0.986707	0.516990	0.816910	-0.870610	0.851572	0.852345	0.723562	0.852871	0.840686	0.943775	0.852536	0.799696	0.735400	0.943181	0.635606	0.624833	0.636283	0.292871	0.510825	0.862492	0.120011	0.689726	0.032616	0.874767	0.533836	0.347690	0.911966	0.628434	0.812367	0.338706	0.396203	0.355418	-0.864122	-0.858170	-0.855347	-0.858909	-0.863480	0.993289	0.993289	0.993289	0.997293	1.000000	1.000000	1.000000	0.997293	0.997128	0.997128	0.997128	0.993663	0.970026	0.970026	0.970026	0.993663	0.854643	0.813408	0.813408	0.813408	0.854643	0.809238	0.698344	0.698344	0.698344	0.809238
rf	0.009109	-0.426990	-0.379890	0.324193	0.310361	0.213569	0.148277	0.346500	0.753949	-0.028186	0.003806	0.902720	0.951668	0.960350	0.986707	0.516990	0.816910	-0.870610	0.851572	0.852345	0.723562	0.852871	0.840686	0.943775	0.852536	0.799696	0.735400	0.943181	0.635606	0.624833	0.636283	0.292871	0.510825	0.862492	0.120011	0.689726	0.032616	0.874767	0.533836	0.347690	0.911966	0.628434	0.812367	0.338706	0.396203	0.355418	-0.864122	-0.858170	-0.855347	-0.858909	-0.863480	0.993289	0.993289	0.993289	0.997293	1.000000	1.000000	1.000000	0.997293	0.997128	0.997128	0.997128	0.993663	0.970026	0.970026	0.970026	0.993663	0.854643	0.813408	0.813408	0.813408	0.854643	0.809238	0.698344	0.698344	0.698344	0.809238
rw	-0.003591	-0.455301	-0.407746	0.314071	0.306260	0.207746	0.139487	0.344032	0.757659	-0.026383	0.004863	0.916727	0.938259	0.965312	0.992113	0.523097	0.808134	-0.870797	0.875131	0.838406	0.702986	0.853626	0.829032	0.950743	0.857413	0.800120	0.741146	0.943087	0.661794	0.647820	0.661655	0.281018	0.537243	0.844244	0.118867	0.695161	0.004797	0.864848	0.526201	0.354886	0.904368	0.624646	0.799166	0.349419	0.405141	0.368155	-0.864253	-0.858322	-0.855536	-0.859059	-0.863498	0.985759	0.985759	0.985759	1.000000	0.997293	0.997293	0.997293	1.000000	0.996731	0.996731	0.996731	0.997319	0.970024	0.970024	0.970024	0.997319	0.863179	0.816256	0.816256	0.816256	0.863179	0.817004	0.699402	0.699402	0.699402	0.817004
lam	0.013235	-0.438802	-0.392627	0.328978	0.313618	0.214946	0.149761	0.345704	0.749065	-0.033223	0.003808	0.897215	0.936173	0.975713	0.988354	0.543815	0.817815	-0.871464	0.862944	0.830024	0.714178	0.875312	0.826355	0.942851	0.856633	0.807038	0.768470	0.947908	0.626725	0.612193	0.640666	0.295602	0.521549	0.850102	0.115986	0.695555	0.023525	0.868562	0.545512	0.380442	0.898125	0.643983	0.799480	0.369532	0.427256	0.386849	-0.864870	-0.858960	-0.856093	-0.859540	-0.864134	0.985274	0.985274	0.985274	0.996731	0.997128	0.997128	0.997128	0.996731	1.000000	1.000000	1.000000	0.996390	0.981111	0.981111	0.981111	0.996390	0.871561	0.834151	0.834151	0.834151	0.871561	0.827104	0.716930	0.716930	0.716930	0.827104
cam	0.013235	-0.438802	-0.392627	0.328978	0.313618	0.214946	0.149761	0.345704	0.749065	-0.033223	0.003808	0.897215	0.936173	0.975713	0.988354	0.543815	0.817815	-0.871464	0.862944	0.830024	0.714178	0.875312	0.826355	0.942851	0.856633	0.807038	0.768470	0.947908	0.626725	0.612193	0.640666	0.295602	0.521549	0.850102	0.115986	0.695555	0.023525	0.868562	0.545512	0.380442	0.898125	0.643983	0.799480	0.369532	0.427256	0.386849	-0.864870	-0.858960	-0.856093	-0.859540	-0.864134	0.985274	0.985274	0.985274	0.996731	0.997128	0.997128	0.997128	0.996731	1.000000	1.000000	1.000000	0.996390	0.981111	0.981111	0.981111	0.996390	0.871561	0.834151	0.834151	0.834151	0.871561	0.827104	0.716930	0.716930	0.716930	0.827104
ram	0.013235	-0.438802	-0.392627	0.328978	0.313618	0.214946	0.149761	0.345704	0.749065	-0.033223	0.003808	0.897215	0.936173	0.975713	0.988354	0.543815	0.817815	-0.871464	0.862944	0.830024	0.714178	0.875312	0.826355	0.942851	0.856633	0.807038	0.768470	0.947908	0.626725	0.612193	0.640666	0.295602	0.521549	0.850102	0.115986	0.695555	0.023525	0.868562	0.545512	0.380442	0.898125	0.643983	0.799480	0.369532	0.427256	0.386849	-0.864870	-0.858960	-0.856093	-0.859540	-0.864134	0.985274	0.985274	0.985274	0.996731	0.997128	0.997128	0.997128	0.996731	1.000000	1.000000	1.000000	0.996390	0.981111	0.981111	0.981111	0.996390	0.871561	0.834151	0.834151	0.834151	0.871561	0.827104	0.716930	0.716930	0.716930	0.827104
lm	0.001210	-0.445813	-0.400793	0.319067	0.306790	0.205865	0.138593	0.335959	0.741494	-0.034475	0.005199	0.918541	0.918355	0.975841	0.990349	0.571736	0.832759	-0.883713	0.881982	0.807571	0.719612	0.867623	0.807050	0.943085	0.850916	0.795987	0.765193	0.943578	0.649924	0.638277	0.643769	0.282749	0.528261	0.836326	0.126938	0.720161	0.024305	0.846400	0.558113	0.404170	0.887381	0.616911	0.781754	0.401499	0.456293	0.419834	-0.876887	-0.871058	-0.868190	-0.871543	-0.876241	0.981555	0.981555	0.981555	0.997319	0.993663	0.993663	0.993663	0.997319	0.996390	0.996390	0.996390	1.000000	0.982114	0.982114	0.982114	1.000000	0.892433	0.849919	0.849919	0.849919	0.892433	0.850105	0.739007	0.739007	0.739007	0.850105
lcm	0.050825	-0.391435	-0.350573	0.355663	0.317066	0.214490	0.159101	0.321143	0.684234	-0.058140	0.000444	0.868593	0.876037	0.987252	0.962021	0.683010	0.874412	-0.887769	0.859988	0.739998	0.758542	0.900025	0.760627	0.898746	0.827431	0.791672	0.823421	0.933271	0.554812	0.547184	0.570597	0.313722	0.473680	0.828000	0.140109	0.737206	0.093435	0.822542	0.639390	0.530829	0.835864	0.614825	0.746818	0.519940	0.576335	0.534961	-0.880697	-0.875173	-0.871984	-0.874876	-0.880201	0.959796	0.959796	0.959796	0.970024	0.970026	0.970026	0.970026	0.970024	0.981111	0.981111	0.981111	0.982114	1.000000	1.000000	1.000000	0.982114	0.939707	0.920290	0.920290	0.920290	0.939707	0.908444	0.825073	0.825073	0.825073	0.908444
cm	0.050825	-0.391435	-0.350573	0.355663	0.317066	0.214490	0.159101	0.321143	0.684234	-0.058140	0.000444	0.868593	0.876037	0.987252	0.962021	0.683010	0.874412	-0.887769	0.859988	0.739998	0.758542	0.900025	0.760627	0.898746	0.827431	0.791672	0.823421	0.933271	0.554812	0.547184	0.570597	0.313722	0.473680	0.828000	0.140109	0.737206	0.093435	0.822542	0.639390	0.530829	0.835864	0.614825	0.746818	0.519940	0.576335	0.534961	-0.880697	-0.875173	-0.871984	-0.874876	-0.880201	0.959796	0.959796	0.959796	0.970024	0.970026	0.970026	0.970026	0.970024	0.981111	0.981111	0.981111	0.982114	1.000000	1.000000	1.000000	0.982114	0.939707	0.920290	0.920290	0.920290	0.939707	0.908444	0.825073	0.825073	0.825073	0.908444
rcm	0.050825	-0.391435	-0.350573	0.355663	0.317066	0.214490	0.159101	0.321143	0.684234	-0.058140	0.000444	0.868593	0.876037	0.987252	0.962021	0.683010	0.874412	-0.887769	0.859988	0.739998	0.758542	0.900025	0.760627	0.898746	0.827431	0.791672	0.823421	0.933271	0.554812	0.547184	0.570597	0.313722	0.473680	0.828000	0.140109	0.737206	0.093435	0.822542	0.639390	0.530829	0.835864	0.614825	0.746818	0.519940	0.576335	0.534961	-0.880697	-0.875173	-0.871984	-0.874876	-0.880201	0.959796	0.959796	0.959796	0.970024	0.970026	0.970026	0.970026	0.970024	0.981111	0.981111	0.981111	0.982114	1.000000	1.000000	1.000000	0.982114	0.939707	0.920290	0.920290	0.920290	0.939707	0.908444	0.825073	0.825073	0.825073	0.908444
rm	0.001210	-0.445813	-0.400793	0.319067	0.306790	0.205865	0.138593	0.335959	0.741494	-0.034475	0.005199	0.918541	0.918355	0.975841	0.990349	0.571736	0.832759	-0.883713	0.881982	0.807571	0.719612	0.867623	0.807050	0.943085	0.850916	0.795987	0.765193	0.943578	0.649924	0.638277	0.643769	0.282749	0.528261	0.836326	0.126938	0.720161	0.024305	0.846400	0.558113	0.404170	0.887381	0.616911	0.781754	0.401499	0.456293	0.419834	-0.876887	-0.871058	-0.868190	-0.871543	-0.876241	0.981555	0.981555	0.981555	0.997319	0.993663	0.993663	0.993663	0.997319	0.996390	0.996390	0.996390	1.000000	0.982114	0.982114	0.982114	1.000000	0.892433	0.849919	0.849919	0.849919	0.892433	0.850105	0.739007	0.739007	0.739007	0.850105
lwb	0.046002	-0.325603	-0.298411	0.314825	0.270101	0.164308	0.122380	0.239683	0.531699	-0.078242	0.001424	0.832935	0.698758	0.908033	0.860507	0.875960	0.915032	-0.881761	0.808197	0.528117	0.781133	0.817747	0.577657	0.772177	0.702371	0.672190	0.769808	0.824830	0.493498	0.501434	0.463525	0.260861	0.400874	0.705215	0.196337	0.764014	0.168889	0.652589	0.734497	0.728475	0.668715	0.446975	0.585662	0.749758	0.788744	0.764351	-0.873948	-0.869498	-0.865872	-0.867852	-0.874259	0.850656	0.850656	0.850656	0.863179	0.854643	0.854643	0.854643	0.863179	0.871561	0.871561	0.871561	0.892433	0.939707	0.939707	0.939707	0.892433	1.000000	0.989661	0.989661	0.989661	1.000000	0.995527	0.954171	0.954171	0.954171	0.995527
ldm	0.084582	-0.258504	-0.233983	0.331386	0.267885	0.164751	0.134257	0.223933	0.478510	-0.092148	-0.003559	0.764888	0.656536	0.883237	0.812096	0.911355	0.920885	-0.857812	0.751275	0.477998	0.794573	0.813068	0.535637	0.715617	0.659306	0.645073	0.781576	0.792925	0.398022	0.408964	0.385108	0.273106	0.332768	0.684018	0.196919	0.736461	0.233944	0.617293	0.775185	0.776615	0.616742	0.431218	0.552169	0.791030	0.830142	0.798299	-0.849979	-0.845903	-0.842329	-0.843831	-0.850509	0.814169	0.814169	0.814169	0.816256	0.813408	0.813408	0.813408	0.816256	0.834151	0.834151	0.834151	0.849919	0.920290	0.920290	0.920290	0.849919	0.989661	1.000000	1.000000	1.000000	0.989661	0.992773	0.976520	0.976520	0.976520	0.992773
cdm	0.084582	-0.258504	-0.233983	0.331386	0.267885	0.164751	0.134257	0.223933	0.478510	-0.092148	-0.003559	0.764888	0.656536	0.883237	0.812096	0.911355	0.920885	-0.857812	0.751275	0.477998	0.794573	0.813068	0.535637	0.715617	0.659306	0.645073	0.781576	0.792925	0.398022	0.408964	0.385108	0.273106	0.332768	0.684018	0.196919	0.736461	0.233944	0.617293	0.775185	0.776615	0.616742	0.431218	0.552169	0.791030	0.830142	0.798299	-0.849979	-0.845903	-0.842329	-0.843831	-0.850509	0.814169	0.814169	0.814169	0.816256	0.813408	0.813408	0.813408	0.816256	0.834151	0.834151	0.834151	0.849919	0.920290	0.920290	0.920290	0.849919	0.989661	1.000000	1.000000	1.000000	0.989661	0.992773	0.976520	0.976520	0.976520	0.992773
rdm	0.084582	-0.258504	-0.233983	0.331386	0.267885	0.164751	0.134257	0.223933	0.478510	-0.092148	-0.003559	0.764888	0.656536	0.883237	0.812096	0.911355	0.920885	-0.857812	0.751275	0.477998	0.794573	0.813068	0.535637	0.715617	0.659306	0.645073	0.781576	0.792925	0.398022	0.408964	0.385108	0.273106	0.332768	0.684018	0.196919	0.736461	0.233944	0.617293	0.775185	0.776615	0.616742	0.431218	0.552169	0.791030	0.830142	0.798299	-0.849979	-0.845903	-0.842329	-0.843831	-0.850509	0.814169	0.814169	0.814169	0.816256	0.813408	0.813408	0.813408	0.816256	0.834151	0.834151	0.834151	0.849919	0.920290	0.920290	0.920290	0.849919	0.989661	1.000000	1.000000	1.000000	0.989661	0.992773	0.976520	0.976520	0.976520	0.992773
rwb	0.046002	-0.325603	-0.298411	0.314825	0.270101	0.164308	0.122380	0.239683	0.531699	-0.078242	0.001424	0.832935	0.698758	0.908033	0.860507	0.875960	0.915032	-0.881761	0.808197	0.528117	0.781133	0.817747	0.577657	0.772177	0.702371	0.672190	0.769808	0.824830	0.493498	0.501434	0.463525	0.260861	0.400874	0.705215	0.196337	0.764014	0.168889	0.652589	0.734497	0.728475	0.668715	0.446975	0.585662	0.749758	0.788744	0.764351	-0.873948	-0.869498	-0.865872	-0.867852	-0.874259	0.850656	0.850656	0.850656	0.863179	0.854643	0.854643	0.854643	0.863179	0.871561	0.871561	0.871561	0.892433	0.939707	0.939707	0.939707	0.892433	1.000000	0.989661	0.989661	0.989661	1.000000	0.995527	0.954171	0.954171	0.954171	0.995527
lb	0.049920	-0.285420	-0.264230	0.302586	0.255058	0.150920	0.115759	0.214493	0.477996	-0.083102	0.001553	0.800295	0.643723	0.869601	0.814175	0.914383	0.915544	-0.866853	0.764308	0.466539	0.789995	0.783080	0.521969	0.718024	0.651881	0.627274	0.744346	0.780885	0.450753	0.463781	0.411821	0.245538	0.358966	0.665902	0.209495	0.748729	0.201371	0.598755	0.752965	0.767959	0.612659	0.397495	0.537874	0.797458	0.833216	0.811201	-0.859059	-0.854817	-0.851173	-0.852858	-0.859623	0.809747	0.809747	0.809747	0.817004	0.809238	0.809238	0.809238	0.817004	0.827104	0.827104	0.827104	0.850105	0.908444	0.908444	0.908444	0.850105	0.995527	0.992773	0.992773	0.992773	0.995527	1.000000	0.975842	0.975842	0.975842	1.000000
lcb	0.091531	-0.152169	-0.137809	0.287742	0.217449	0.123135	0.111357	0.161987	0.352840	-0.098735	-0.004776	0.684496	0.527674	0.770362	0.695236	0.966683	0.914284	-0.813820	0.640622	0.341281	0.811381	0.700933	0.407616	0.586209	0.530464	0.524233	0.677501	0.675669	0.298822	0.325201	0.259553	0.222267	0.222051	0.585250	0.236918	0.685520	0.323689	0.484314	0.800709	0.829185	0.486627	0.293680	0.440939	0.868021	0.896043	0.870404	-0.806065	-0.802532	-0.799097	-0.799867	-0.807174	0.713309	0.713309	0.713309	0.699402	0.698344	0.698344	0.698344	0.699402	0.716930	0.716930	0.716930	0.739007	0.825073	0.825073	0.825073	0.739007	0.954171	0.976520	0.976520	0.976520	0.954171	0.975842	1.000000	1.000000	1.000000	0.975842
cb	0.091531	-0.152169	-0.137809	0.287742	0.217449	0.123135	0.111357	0.161987	0.352840	-0.098735	-0.004776	0.684496	0.527674	0.770362	0.695236	0.966683	0.914284	-0.813820	0.640622	0.341281	0.811381	0.700933	0.407616	0.586209	0.530464	0.524233	0.677501	0.675669	0.298822	0.325201	0.259553	0.222267	0.222051	0.585250	0.236918	0.685520	0.323689	0.484314	0.800709	0.829185	0.486627	0.293680	0.440939	0.868021	0.896043	0.870404	-0.806065	-0.802532	-0.799097	-0.799867	-0.807174	0.713309	0.713309	0.713309	0.699402	0.698344	0.698344	0.698344	0.699402	0.716930	0.716930	0.716930	0.739007	0.825073	0.825073	0.825073	0.739007	0.954171	0.976520	0.976520	0.976520	0.954171	0.975842	1.000000	1.000000	1.000000	0.975842
rcb	0.091531	-0.152169	-0.137809	0.287742	0.217449	0.123135	0.111357	0.161987	0.352840	-0.098735	-0.004776	0.684496	0.527674	0.770362	0.695236	0.966683	0.914284	-0.813820	0.640622	0.341281	0.811381	0.700933	0.407616	0.586209	0.530464	0.524233	0.677501	0.675669	0.298822	0.325201	0.259553	0.222267	0.222051	0.585250	0.236918	0.685520	0.323689	0.484314	0.800709	0.829185	0.486627	0.293680	0.440939	0.868021	0.896043	0.870404	-0.806065	-0.802532	-0.799097	-0.799867	-0.807174	0.713309	0.713309	0.713309	0.699402	0.698344	0.698344	0.698344	0.699402	0.716930	0.716930	0.716930	0.739007	0.825073	0.825073	0.825073	0.739007	0.954171	0.976520	0.976520	0.976520	0.954171	0.975842	1.000000	1.000000	1.000000	0.975842
rb	0.049920	-0.285420	-0.264230	0.302586	0.255058	0.150920	0.115759	0.214493	0.477996	-0.083102	0.001553	0.800295	0.643723	0.869601	0.814175	0.914383	0.915544	-0.866853	0.764308	0.466539	0.789995	0.783080	0.521969	0.718024	0.651881	0.627274	0.744346	0.780885	0.450753	0.463781	0.411821	0.245538	0.358966	0.665902	0.209495	0.748729	0.201371	0.598755	0.752965	0.767959	0.612659	0.397495	0.537874	0.797458	0.833216	0.811201	-0.859059	-0.854817	-0.851173	-0.852858	-0.859623	0.809747	0.809747	0.809747	0.817004	0.809238	0.809238	0.809238	0.817004	0.827104	0.827104	0.827104	0.850105	0.908444	0.908444	0.908444	0.850105	0.995527	0.992773	0.992773	0.992773	0.995527	1.000000	0.975842	0.975842	0.975842	1.000000

	player_url	short_name	long_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	wage_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	release_clause_eur	player_tags	team_position	team_jersey_number	loaned_from	joined	contract_valid_until	nation_position	nation_jersey_number	pace	shooting	passing	dribbling	defending	physic	gk_diving	gk_handling	gk_kicking	gk_reflexes	gk_speed	gk_positioning	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	mentality_composure	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
sofifa_id
158023	https://sofifa.com/player/158023/lionel-messi/...	L. Messi	Lionel Andrés Messi Cuccittini	28	1987-06-24	170	72	Argentina	FC Barcelona	94	95	111000000	550000	RW, CF	Left	5	4	4	Medium/Low	Messi	Yes	NaN	#Speedster, #Dribbler, #FK Specialist, #Acroba...	RW	10.0	NaN	2004-07-01	2018.0	RW	10.0	92.0	88.0	86.0	95.0	24.0	62.0	NaN	NaN	NaN	NaN	NaN	NaN	Finesse Shot, Speed Dribbler (CPU AI Only), On...	80-4	93-1	71	88-1	85	96	89	90	79+3	96	95-1	90	92-2	92-2	95	80	68-5	76-1	59-1	88	48	22	90-2	90	74	NaN	13-12	23+2	21+1	6	11	15	14	8	87+3	87+3	87+3	91+3	91+3	91+3	91+3	91+3	91+3	91+3	91+3	90+3	82+3	82+3	82+3	90+3	62+3	57+3	57+3	57+3	62+3	57+3	44+3	44+3	44+3	57+3
20801	https://sofifa.com/player/20801/c-ronaldo-dos-...	Cristiano Ronaldo	Cristiano Ronaldo dos Santos Aveiro	30	1985-02-05	185	80	Portugal	Real Madrid	93	93	85500000	475000	LW, LM	Right	5	4	5	High/Low	C. Ronaldo	Yes	NaN	#Speedster, #Dribbler, #Distance Shooter, #Acr...	LM	7.0	NaN	2009-07-01	2018.0	ST	7.0	92.0	93.0	80.0	91.0	33.0	78.0	NaN	NaN	NaN	NaN	NaN	NaN	Power Free-Kick, Flair, Long Shot Taker (CPU A...	82-1	95	86	81-1	87	93	88	77-2	72	91-1	91	93-1	90-3	92+2	62-1	94	94	87-2	79	93	62-1	29+5	93+2	81	85	NaN	22	31	23	7	11	15	14	11	91+3	91+3	91+3	90+3	91+3	91+3	91+3	90+3	88+3	88+3	88+3	88+3	80+3	80+3	80+3	88+3	64+3	60+3	60+3	60+3	64+3	60+3	52+3	52+3	52+3	60+3
9014	https://sofifa.com/player/9014/arjen-robben/16...	A. Robben	Arjen Robben	31	1984-01-23	180	80	Netherlands	FC Bayern München	90	90	56000000	250000	RM, LM, RW	Left	5	2	4	High/Low	Normal	Yes	NaN	#Speedster, #Dribbler, #Distance Shooter, #Acr...	SUB	10.0	NaN	2009-08-28	2017.0	RW	11.0	92.0	86.0	82.0	92.0	32.0	64.0	NaN	NaN	NaN	NaN	NaN	NaN	Diver, Injury Prone, Avoids Using Weaker Foot,...	80	85	51-1	85-1	86	93	86+1	83	74-2	90-2	92-1	92-1	91-2	91	91	86	61	76-2	65	90	47	39	89	84	80	NaN	29	26	26	10	8	11	5	15	84+3	84+3	84+3	89+3	88+3	88+3	88+3	89+3	88+3	88+3	88+3	87+3	80+3	80+3	80+3	87+3	65+3	60+3	60+3	60+3	65+3	59+3	47+3	47+3	47+3	59+3
167495	https://sofifa.com/player/167495/manuel-neuer/...	M. Neuer	Manuel Neuer	29	1986-03-27	193	92	Germany	FC Bayern München	90	90	58000000	250000	GK	Right	5	4	1	Medium/Medium	Normal	Yes	NaN	NaN	GK	1.0	NaN	2011-07-01	2019.0	GK	1.0	NaN	NaN	NaN	NaN	NaN	NaN	85.0	87.0	91.0	86.0	60.0	90.0	GK Long Throw, 1-on-1 Rush	15-10	13-12	25	48+6	11-14	16-9	14-11	11-14	47+6	31	58	61	43	88	35	25-17	78	44	83	16-9	29	30	12-13	70+20	37	NaN	10-15	10-15	11-14	85-3	87+2	91	90	86	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
176580	https://sofifa.com/player/176580/luis-suarez/1...	L. Suárez	Luis Alberto Suárez Díaz	28	1987-01-24	182	85	Uruguay	FC Barcelona	90	90	69000000	300000	ST	Right	5	4	4	High/Medium	Normal	Yes	NaN	#Acrobat, #Clinical Finisher	ST	9.0	NaN	2014-07-11	2019.0	NaN	NaN	83.0	88.0	79.0	87.0	42.0	79.0	NaN	NaN	NaN	NaN	NaN	NaN	Diver, Beat Offside Trap, Flair, Technical Dri...	77	90-1	77+2	82	87+2	88-2	86	84	64	91+2	88	78-1	86	91	60	88+4	69	86	76	85+3	78	41	91+3	84	85	NaN	30	45	38	27	25	31	33	37	87+3	87+3	87+3	87+3	88+3	88+3	88+3	87+3	86+3	86+3	86+3	85+3	79+3	79+3	79+3	85+3	67+3	65+3	65+3	65+3	67+3	64+3	58+3	58+3	58+3	64+3

	short_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	team_position	team_jersey_number	joined	contract_valid_until	pace	shooting	passing	dribbling	defending	physic	gk_speed	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb
sofifa_id
11728	B. Richardson	45	1969-08-05	185	77	England	Wycombe Wanderers	44	44	10000	GK	Right	1	2	1	Medium/Medium	Stocky	No	SUB	13.0	2014-01-30	2021.0	0.0	0.0	0.0	0.0	0.0	0.0	25.0	NaN	-3	-3	-1	-1	-1	-3	-1	-3	1	23	25	25	38	34	44	1	51	32	47	7	45	7	1	9	5	3	-1	1	37	55	37	59	33	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
645	D. Andersson	42	1972-12-18	187	85	Sweden	Helsingborgs IF	57	57	20000	GK	Right	1	3	1	Medium/Medium	Normal	No	SUB	39.0	2011-01-01	2020.0	0.0	0.0	0.0	0.0	0.0	0.0	12.0	NaN	-1	1	-1	37	-5	1	1	9	32	25	-1	-1	-3	61	25	25	11	1	64	3	34	22	-1	31	-1	-1	11	13	59	52	50	59	63	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
165854	J. Henao	43	1971-12-30	181	79	Colombia	Once Caldas	65	65	90000	GK	Right	1	3	1	Medium/Medium	Stocky	No	SUB	1.0	2010-07-01	2016.0	0.0	0.0	0.0	0.0	0.0	0.0	53.0	Team Player	3	-5	5	34	5	13	3	24	48	25	52	53	59	60	57	1	72	34	60	3	32	22	1	27	11	3	1	-1	72	70	58	67	62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
140029	O. Pérez	42	1973-02-01	172	75	Mexico	Pachuca	70	70	230000	GK	Right	2	3	1	Medium/Medium	Stocky	No	GK	21.0	2015-07-01	2016.0	0.0	0.0	0.0	0.0	0.0	0.0	58.0	GK Up for Corners, GK Long Throw	13	3	15	27	1	-1	-1	15	28	20	71	66	69	73	69	18	85	0	66	11	10	-1	-1	55	24	-3	-1	-3	71	65	66	73	74	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
31834	F. Johnsen	41	1974-03-17	187	79	Norway	Odds BK	66	66	0	ST	Right	1	4	3	Medium/Medium	Lean	No	SUB	11.0	2010-01-01	2015.0	39.0	70.0	52.0	56.0	41.0	62.0	0.0	Power Header	54	72	84	53	73	51	62	52	44	61	15	22	62	53	57	75	70	48	74	66	49	33	77	55	61	33	44	43	15	13	10	9	15	66	66	66	56	60	60	60	56	57	57	57	54	53	53	53	54	45	46	46	46	45	45	50	50	50	45

	Club	Club_country	League
0	Bournemouth	England	Premier League
1	Arsenal	England	Premier League
2	Aston Villa	England	Premier League
3	Chelsea	England	Premier League
4	Crystal Palace	England	Premier League
5	Everton	England	Premier League
6	Leicester City	England	Premier League
7	Liverpool	England	Premier League
8	Manchester City	England	Premier League
9	Manchester United	England	Premier League
10	Newcastle United	England	Premier League
11	Norwich City	England	Premier League
12	Southampton	England	Premier League
13	Stoke City	England	Premier League
14	Sunderland	England	Premier League
15	Swansea City	England	Premier League
16	Tottenham Hotspur	England	Premier League
17	Watford	England	Premier League
18	West Bromwich Albion	England	Premier League
19	West Ham United	England	Premier League
20	Atalanta	Italy	Serie A
21	Bologna	Italy	Serie A
22	Carpi	Italy	Serie A
23	Chievo Verona	Italy	Serie A
24	Empoli	Italy	Serie A
25	Fiorentina	Italy	Serie A
26	Frosinone	Italy	Serie A
27	Genoa	Italy	Serie A
28	Hellas Verona	Italy	Serie A
29	Inter	Italy	Serie A
30	Juventus	Italy	Serie A
31	Lazio	Italy	Serie A
32	Milan	Italy	Serie A
33	Napoli	Italy	Serie A
34	Palermo	Italy	Serie A
35	Roma	Italy	Serie A
36	Sampdoria	Italy	Serie A
37	Sassuolo	Italy	Serie A
38	Torino	Italy	Serie A
39	Udinese	Italy	Serie A
40	FC Augsburg	Germany	Bundesliga
41	Bayer 04 Leverkusen	Germany	Bundesliga
42	FC Bayern München	Germany	Bundesliga
43	Borussia Dortmund	Germany	Bundesliga
44	Borussia Mönchengladbach	Germany	Bundesliga
45	SV Darmstadt 98	Germany	Bundesliga
46	Eintracht Frankfurt	Germany	Bundesliga
47	Hamburger SV	Germany	Bundesliga
48	Hannover 96	Germany	Bundesliga
49	Hertha BSC	Germany	Bundesliga
50	TSG 1899 Hoffenheim	Germany	Bundesliga
51	FC Ingolstadt 04	Germany	Bundesliga
52	1. FC Köln	Germany	Bundesliga
53	1. FSV Mainz 05	Germany	Bundesliga
54	FC Schalke 04	Germany	Bundesliga
55	VfB Stuttgart	Germany	Bundesliga
56	SV Werder Bremen	Germany	Bundesliga
57	VfL Wolfsburg	Germany	Bundesliga
58	Bilbao Athletic	Spain	La Liga
59	Atlético Madrid	Spain	La Liga
60	FC Barcelona	Spain	La Liga
61	RC Celta	Spain	La Liga
62	Deportivo de La Coruña	Spain	La Liga
63	SD Eibar	Spain	La Liga
64	RCD Espanyol	Spain	La Liga
65	Getafe CF	Spain	La Liga
66	Granada CF	Spain	La Liga
67	UD Las Palmas	Spain	La Liga
68	Levante UD	Spain	La Liga
69	Málaga	Spain	La Liga
70	Rayo Vallecano	Spain	La Liga
71	Real Betis	Spain	La Liga
72	Real Madrid	Spain	La Liga
73	Real Sociedad	Spain	La Liga
74	Sevilla FC	Spain	La Liga
75	Real Sporting de Gijón	Spain	La Liga
76	Valencia CF	Spain	La Liga
77	Villarreal CF	Spain	La Liga
78	Angers SCO	France	Ligue 1
79	Sporting Club de Bastia	France	Ligue 1
80	FC Girondins de Bordeaux	France	Ligue 1
81	Stade Malherbe Caen	France	Ligue 1
82	GFC Ajaccio	France	Ligue 1
83	En Avant de Guingamp	France	Ligue 1
84	LOSC Lille	France	Ligue 1
85	FC Lorient	France	Ligue 1
86	Olympique Lyonnais	France	Ligue 1
87	Olympique de Marseille	France	Ligue 1
88	AS Monaco	France	Ligue 1
89	Montpellier HSC	France	Ligue 1
90	FC Nantes	France	Ligue 1
91	OGC Nice	France	Ligue 1
92	Paris Saint-Germain	France	Ligue 1
93	Stade de Reims	France	Ligue 1
94	Stade Rennais FC	France	Ligue 1
95	AS Saint-Étienne	France	Ligue 1
96	Toulouse Football Club	France	Ligue 1
97	ESTAC Troyes	France	Ligue 1

	short_name	age	dob	height_cm	weight_kg	nationality	club	overall	potential	value_eur	player_positions	preferred_foot	international_reputation	weak_foot	skill_moves	work_rate	body_type	real_face	team_position	team_jersey_number	joined	contract_valid_until	pace	shooting	passing	dribbling	defending	physic	gk_speed	player_traits	attacking_crossing	attacking_finishing	attacking_heading_accuracy	attacking_short_passing	attacking_volleys	skill_dribbling	skill_curve	skill_fk_accuracy	skill_long_passing	skill_ball_control	movement_acceleration	movement_sprint_speed	movement_agility	movement_reactions	movement_balance	power_shot_power	power_jumping	power_stamina	power_strength	power_long_shots	mentality_aggression	mentality_interceptions	mentality_positioning	mentality_vision	mentality_penalties	defending_marking	defending_standing_tackle	defending_sliding_tackle	goalkeeping_diving	goalkeeping_handling	goalkeeping_kicking	goalkeeping_positioning	goalkeeping_reflexes	ls	st	rs	lw	lf	cf	rf	rw	lam	cam	ram	lm	lcm	cm	rcm	rm	lwb	ldm	cdm	rdm	rwb	lb	lcb	cb	rcb	rb	Club	Club_country	League
2418	Valerón	40	1975-06-17	186	76	Spain	UD Las Palmas	74	74	0	CAM, CM	Right	2	4	4	Medium/Low	Lean	Yes	SUB	21.0	2013-07-15	2016.0	34.0	59.0	80.0	76.0	23.0	36.0	0.0	Injury Free, Playmaker (CPU AI Only), Swerve P...	73	61	64	89	60	80	73	68	74	85	30	32	49	82	65	50	27	19	43	64	35	28	65	89	67	3	15	3	8	9	15	10	9	63	63	63	69	70	70	70	69	74	74	74	69	70	70	70	69	47	50	50	50	47	42	36	36	36	42	UD Las Palmas	Spain	La Liga
1774	A. Manninger	38	1977-06-04	189	85	Austria	FC Augsburg	72	72	350000	GK	Right	2	2	1	Medium/Medium	Normal	Yes	SUB	1.0	2012-11-21	2016.0	0.0	0.0	0.0	0.0	0.0	0.0	40.0	Puncher, Team Player	-1	-1	1	23	3	1	5	5	9	0	39	40	48	77	45	23	70	31	74	-3	40	7	-7	3	9	-3	3	5	73	73	65	73	75	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	FC Augsburg	Germany	Bundesliga
1394	M. Schwarzer	42	1972-10-06	194	95	Australia	Leicester City	73	73	500000	GK	Right	2	3	1	Medium/Medium	Normal	No	SUB	32.0	2015-01-06	2016.0	0.0	0.0	0.0	0.0	0.0	0.0	48.0	NaN	1	-3	3	25	9	1	3	-3	24	9	50	46	36	76	43	30	57	-7	70	11	41	23	13	42	28	9	13	15	69	73	68	79	71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Leicester City	England	Premier League
2603	B. Nivet	38	1977-01-02	178	75	France	ESTAC Troyes	73	73	300000	CAM, CM	Right	2	3	3	Medium/Medium	Normal	No	SUB	10.0	2012-06-12	2016.0	59.0	70.0	74.0	72.0	55.0	61.0	0.0	Playmaker (CPU AI Only), Team Player	63	73	59	79	65	75	74	74	74	80	54	60	64	71	72	72	24	59	70	68	59	55	70	82	77	54	60	46	12	8	9	15	8	69	69	69	70	71	71	71	70	73	73	73	70	72	72	72	70	61	65	65	65	61	60	58	58	58	60	ESTAC Troyes	France	Ligue 1
857	L. Castellazzi	39	1975-07-19	192	89	Italy	Torino	72	72	350000	GK	Left	2	2	1	Medium/Medium	Lean	No	RES	13.0	2014-09-01	2016.0	0.0	0.0	0.0	0.0	0.0	0.0	33.0	Puncher	-1	-3	-3	32	-3	7	13	1	28	23	9	13	53	66	55	5	59	31	76	5	30	23	3	45	6	1	-1	7	76	76	66	84	66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	Torino	Italy	Serie A

sofifa_id	158023	20801	9014	167495	176580
short_name	L. Messi	Cristiano Ronaldo	A. Robben	M. Neuer	L. Suárez
age	28	30	31	29	28
dob	1987-06-24	1985-02-05	1984-01-23	1986-03-27	1987-01-24
height_cm	170	185	180	193	182
weight_kg	72	80	80	92	85
nationality	Argentina	Portugal	Netherlands	Germany	Uruguay
club	FC Barcelona	Real Madrid	FC Bayern München	FC Bayern München	FC Barcelona
overall	94	93	90	90	90
potential	95	93	90	90	90
value_eur	111000000	85500000	56000000	58000000	69000000
player_positions	RW, CF	LW, LM	RM, LM, RW	GK	ST
preferred_foot	Left	Right	Left	Right	Right
international_reputation	5	5	5	5	5
weak_foot	4	4	2	4	4
skill_moves	4	5	4	1	4
work_rate	Medium/Low	High/Low	High/Low	Medium/Medium	High/Medium
body_type	Messi	C. Ronaldo	Normal	Normal	Normal
real_face	Yes	Yes	Yes	Yes	Yes
team_position	RW	LM	SUB	GK	ST
team_jersey_number	10	7	10	1	9
joined	2004-07-01	2009-07-01	2009-08-28	2011-07-01	2014-07-11
contract_valid_until	2018	2018	2017	2019	2019
pace	92	92	92	0	83
shooting	88	93	86	0	88
passing	86	80	82	0	79
dribbling	95	91	92	0	87
defending	24	33	32	0	42
physic	62	78	64	0	79
gk_speed	0	0	0	60	0
player_traits	Finesse Shot, Speed Dribbler (CPU AI Only), On...	Power Free-Kick, Flair, Long Shot Taker (CPU A...	Diver, Injury Prone, Avoids Using Weaker Foot,...	GK Long Throw, 1-on-1 Rush	Diver, Beat Offside Trap, Flair, Technical Dri...
attacking_crossing	76	81	80	5	77
attacking_finishing	92	95	85	1	89
attacking_heading_accuracy	71	86	50	25	79
attacking_short_passing	87	80	84	54	82
attacking_volleys	85	87	86	-3	89
skill_dribbling	96	93	93	7	86
skill_curve	89	88	87	3	86
skill_fk_accuracy	90	75	83	-3	84
skill_long_passing	82	72	72	53	64
skill_ball_control	96	90	88	31	93
movement_acceleration	94	91	91	58	88
movement_sprint_speed	90	92	91	61	77
movement_agility	90	87	89	43	86
movement_reactions	90	94	91	88	91
movement_balance	95	61	91	35	60
power_shot_power	80	94	86	8	92
power_jumping	63	94	61	78	69
power_stamina	75	85	74	44	86
power_strength	58	79	65	83	76
power_long_shots	88	93	90	7	88
mentality_aggression	48	61	47	29	78
mentality_interceptions	22	34	39	30	41
mentality_positioning	88	95	89	-1	94
mentality_vision	90	81	84	90	84
mentality_penalties	74	85	80	37	85
defending_marking	1	22	29	-5	30
defending_standing_tackle	25	31	26	-5	45
defending_sliding_tackle	22	23	26	-3	38
goalkeeping_diving	6	7	10	82	27
goalkeeping_handling	11	11	8	89	25
goalkeeping_kicking	15	15	11	91	31
goalkeeping_positioning	14	14	5	90	33
goalkeeping_reflexes	8	11	15	86	37
ls	90	94	87	0	90
st	90	94	87	0	90
rs	90	94	87	0	90
lw	94	93	92	0	90
lf	94	94	91	0	91
cf	94	94	91	0	91
rf	94	94	91	0	91
rw	94	93	92	0	90
lam	94	91	91	0	89
cam	94	91	91	0	89
ram	94	91	91	0	89
lm	93	91	90	0	88
lcm	85	83	83	0	82
cm	85	83	83	0	82
rcm	85	83	83	0	82
rm	93	91	90	0	88
lwb	65	67	68	0	70
ldm	60	63	63	0	68
cdm	60	63	63	0	68
rdm	60	63	63	0	68
rwb	65	67	68	0	70
lb	60	63	62	0	67
lcb	47	55	50	0	61
cb	47	55	50	0	61
rcb	47	55	50	0	61
rb	60	63	62	0	67

	short_name	nationality	value_eur	CountryName	CapitalName	CapitalLatitude	CapitalLongitude	CountryCode	ContinentName
0	L. Messi	Argentina	111000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
1	S. Agüero	Argentina	47500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
2	J. Pastore	Argentina	34500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
3	G. Higuaín	Argentina	34500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
4	C. Tévez	Argentina	34500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
5	A. Di María	Argentina	34000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
6	N. Gaitán	Argentina	25500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
7	N. Otamendi	Argentina	24000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
8	R. Pereyra	Argentina	23000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
9	E. Garay	Argentina	21000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
10	M. Musacchio	Argentina	20000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
11	E. Banega	Argentina	20000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
12	E. Salvio	Argentina	18000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
13	P. Piatti	Argentina	17500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
14	M. Rojo	Argentina	17500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
15	L. Biglia	Argentina	15000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
16	L. Vietto	Argentina	15000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
17	J. Mascherano	Argentina	15000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
18	M. Icardi	Argentina	14500000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
19	P. Zabaleta	Argentina	14000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
20	P. Dybala	Argentina	14000000	Argentina	Buenos Aires	-34.583333	-58.666667	AR	South America
21	Cristiano Ronaldo	Portugal	85500000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
22	João Moutinho	Portugal	23000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
23	Nani	Portugal	21000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
24	William Carvalho	Portugal	20500000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
25	André Gomes	Portugal	20000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
26	A. Lopes	Portugal	19500000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
27	Danny	Portugal	17000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
28	Adrien Silva	Portugal	17000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
29	Pepe	Portugal	17000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
30	Rui Patrício	Portugal	16500000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
31	Bernardo Silva	Portugal	14000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
32	Quaresma	Portugal	14000000	Portugal	Lisbon	38.716667	-9.133333	PT	Europe
33	E. Hazard	Belgium	74000000	Belgium	Brussels	50.833333	4.333333	BE	Europe
34	K. De Bruyne	Belgium	50500000	Belgium	Brussels	50.833333	4.333333	BE	Europe
35	T. Courtois	Belgium	44000000	Belgium	Brussels	50.833333	4.333333	BE	Europe
36	C. Benteke	Belgium	27000000	Belgium	Brussels	50.833333	4.333333	BE	Europe
37	R. Nainggolan	Belgium	26500000	Belgium	Brussels	50.833333	4.333333	BE	Europe
38	V. Kompany	Belgium	25000000	Belgium	Brussels	50.833333	4.333333	BE	Europe
39	D. Mertens	Belgium	22000000	Belgium	Brussels	50.833333	4.333333	BE	Europe